Overview

Brought to you by YData

Dataset statistics

Number of variables58
Number of observations239,661
Missing cells2,680,168
Missing cells (%)19.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory224.8 MiB
Average record size in memory983.7 B

Variable types

Categorical43
Text2
Numeric12
Unsupported1

Alerts

C1M_School.closing is highly overall correlated with C2M_Workplace.closing and 4 other fieldsHigh correlation
C2M_Flag is highly overall correlated with C3M_Flag and 1 other fieldsHigh correlation
C2M_Workplace.closing is highly overall correlated with C1M_School.closing and 4 other fieldsHigh correlation
C3M_Cancel.public.events is highly overall correlated with C1M_School.closing and 6 other fieldsHigh correlation
C3M_Flag is highly overall correlated with C2M_Flag and 1 other fieldsHigh correlation
C4M_Flag is highly overall correlated with C2M_Flag and 1 other fieldsHigh correlation
C4M_Restrictions.on.gatherings is highly overall correlated with C3M_Cancel.public.events and 1 other fieldsHigh correlation
C5M_Close.public.transport is highly overall correlated with ContainmentHealthIndex_Average and 2 other fieldsHigh correlation
C6M_Stay.at.home.requirements is highly overall correlated with C3M_Cancel.public.events and 3 other fieldsHigh correlation
C7M_Flag is highly overall correlated with CityCode and 1 other fieldsHigh correlation
C7M_Restrictions.on.internal.movement is highly overall correlated with ContainmentHealthIndex_Average and 2 other fieldsHigh correlation
C8EV_International.travel.controls is highly overall correlated with MajorityVaccinatedHigh correlation
CityCode is highly overall correlated with C7M_Flag and 19 other fieldsHigh correlation
CityName is highly overall correlated with C7M_Flag and 19 other fieldsHigh correlation
ConfirmedCases is highly overall correlated with CityCode and 4 other fieldsHigh correlation
ConfirmedDeaths is highly overall correlated with CityCode and 2 other fieldsHigh correlation
ContainmentHealthIndex_Average is highly overall correlated with C1M_School.closing and 9 other fieldsHigh correlation
CountryCode is highly overall correlated with CityCode and 4 other fieldsHigh correlation
CountryName is highly overall correlated with CityCode and 4 other fieldsHigh correlation
Date is highly overall correlated with ConfirmedCases and 4 other fieldsHigh correlation
E1_Flag is highly overall correlated with CityCode and 4 other fieldsHigh correlation
E1_Income.support is highly overall correlated with EconomicSupportIndexHigh correlation
E2_Debt.contract.relief is highly overall correlated with CityCode and 2 other fieldsHigh correlation
E3_Fiscal.measures is highly overall correlated with CityCode and 2 other fieldsHigh correlation
E4_International.support is highly overall correlated with CityCode and 2 other fieldsHigh correlation
EconomicSupportIndex is highly overall correlated with E1_Flag and 4 other fieldsHigh correlation
GovernmentResponseIndex_Average is highly overall correlated with C1M_School.closing and 10 other fieldsHigh correlation
H1_Flag is highly overall correlated with CityCode and 1 other fieldsHigh correlation
H1_Public.information.campaigns is highly overall correlated with ContainmentHealthIndex_Average and 2 other fieldsHigh correlation
H3_Contact.tracing is highly overall correlated with CityCode and 3 other fieldsHigh correlation
H4_Emergency.investment.in.healthcare is highly overall correlated with CityCode and 8 other fieldsHigh correlation
H5_Investment.in.vaccines is highly overall correlated with CityCode and 1 other fieldsHigh correlation
H7_Flag is highly overall correlated with CityCode and 4 other fieldsHigh correlation
H7_Vaccination.policy is highly overall correlated with ConfirmedCases and 9 other fieldsHigh correlation
Jurisdiction is highly overall correlated with CityCode and 1 other fieldsHigh correlation
MajorityVaccinated is highly overall correlated with C8EV_International.travel.controls and 12 other fieldsHigh correlation
StringencyIndex_Average is highly overall correlated with C1M_School.closing and 11 other fieldsHigh correlation
V1_Vaccine.Prioritisation..summary. is highly overall correlated with Date and 4 other fieldsHigh correlation
V2A_Vaccine.Availability..summary. is highly overall correlated with CityCode and 12 other fieldsHigh correlation
V2B_Vaccine.age.eligibility.availability.age.floor..general.population.summary. is highly overall correlated with H4_Emergency.investment.in.healthcare and 8 other fieldsHigh correlation
V2C_Vaccine.age.eligibility.availability.age.floor..at.risk.summary. is highly overall correlated with H4_Emergency.investment.in.healthcare and 6 other fieldsHigh correlation
V2D_Medically..clinically.vulnerable..Non.elderly. is highly overall correlated with H4_Emergency.investment.in.healthcare and 7 other fieldsHigh correlation
V2E_Education is highly overall correlated with H4_Emergency.investment.in.healthcare and 7 other fieldsHigh correlation
V2F_Frontline.workers...non.healthcare. is highly overall correlated with CityCode and 8 other fieldsHigh correlation
V2G_Frontline.workers...healthcare. is highly overall correlated with H4_Emergency.investment.in.healthcare and 2 other fieldsHigh correlation
V3_Vaccine.Financial.Support..summary. is highly overall correlated with CityCode and 5 other fieldsHigh correlation
V4_Mandatory.Vaccination..summary. is highly overall correlated with CityCode and 1 other fieldsHigh correlation
H1_Public.information.campaigns is highly imbalanced (78.0%) Imbalance
H1_Flag is highly imbalanced (95.8%) Imbalance
H6M_Flag is highly imbalanced (50.0%) Imbalance
H7_Flag is highly imbalanced (98.6%) Imbalance
H8M_Flag is highly imbalanced (52.7%) Imbalance
V2F_Frontline.workers...non.healthcare. is highly imbalanced (58.4%) Imbalance
V2G_Frontline.workers...healthcare. is highly imbalanced (66.0%) Imbalance
RegionName has 7672 (3.2%) missing values Missing
RegionCode has 7672 (3.2%) missing values Missing
CityName has 195821 (81.7%) missing values Missing
CityCode has 195821 (81.7%) missing values Missing
C1M_Flag has 54711 (22.8%) missing values Missing
C2M_Flag has 76485 (31.9%) missing values Missing
C3M_Flag has 76752 (32.0%) missing values Missing
C4M_Flag has 103505 (43.2%) missing values Missing
C5M_Flag has 145190 (60.6%) missing values Missing
C6M_Flag has 125301 (52.3%) missing values Missing
C7M_Flag has 102801 (42.9%) missing values Missing
E1_Flag has 96993 (40.5%) missing values Missing
E3_Fiscal.measures has 194695 (81.2%) missing values Missing
E4_International.support has 195213 (81.5%) missing values Missing
H1_Flag has 8924 (3.7%) missing values Missing
H4_Emergency.investment.in.healthcare has 190612 (79.5%) missing values Missing
H5_Investment.in.vaccines has 135859 (56.7%) missing values Missing
H6M_Flag has 24127 (10.1%) missing values Missing
H7_Flag has 98761 (41.2%) missing values Missing
H8M_Flag has 41218 (17.2%) missing values Missing
V2B_Vaccine.age.eligibility.availability.age.floor..general.population.summary. has 100329 (41.9%) missing values Missing
V2C_Vaccine.age.eligibility.availability.age.floor..at.risk.summary. has 100072 (41.8%) missing values Missing
V2D_Medically..clinically.vulnerable..Non.elderly. has 91773 (38.3%) missing values Missing
V2E_Education has 91773 (38.3%) missing values Missing
V2F_Frontline.workers...non.healthcare. has 91773 (38.3%) missing values Missing
V2G_Frontline.workers...healthcare. has 91773 (38.3%) missing values Missing
V4_Mandatory.Vaccination..summary. has 3114 (1.3%) missing values Missing
ConfirmedCases has 15569 (6.5%) missing values Missing
ConfirmedDeaths has 15859 (6.6%) missing values Missing
E3_Fiscal.measures is highly skewed (γ1 = 90.04818954) Skewed
E4_International.support is highly skewed (γ1 = 102.363522) Skewed
H4_Emergency.investment.in.healthcare is highly skewed (γ1 = 148.7899257) Skewed
H5_Investment.in.vaccines is highly skewed (γ1 = 299.1536436) Skewed
CityName is uniformly distributed Uniform
CityCode is uniformly distributed Uniform
PopulationVaccinated is an unsupported type, check if it needs cleaning or further analysis Unsupported
E3_Fiscal.measures has 44491 (18.6%) zeros Zeros
E4_International.support has 44396 (18.5%) zeros Zeros
H4_Emergency.investment.in.healthcare has 48741 (20.3%) zeros Zeros
H5_Investment.in.vaccines has 103461 (43.2%) zeros Zeros
H7_Vaccination.policy has 80733 (33.7%) zeros Zeros
ConfirmedCases has 12826 (5.4%) zeros Zeros
ConfirmedDeaths has 24940 (10.4%) zeros Zeros
StringencyIndex_Average has 6168 (2.6%) zeros Zeros
GovernmentResponseIndex_Average has 5698 (2.4%) zeros Zeros
ContainmentHealthIndex_Average has 5747 (2.4%) zeros Zeros
EconomicSupportIndex has 62031 (25.9%) zeros Zeros

Reproduction

Analysis started2025-08-18 14:37:28.526921
Analysis finished2025-08-18 14:42:16.103895
Duration4 minutes and 47.58 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CountryName
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
Brazil
59184 
United States
56992 
India
40552 
China
36901 
Australia
25208 
Other values (2)
20824 

Length

Max length14
Median length13
Mean length7.8399114
Min length5

Characters and Unicode

Total characters1,878,921
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralia
2nd rowAustralia
3rd rowAustralia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Brazil 59184
24.7%
United States 56992
23.8%
India 40552
16.9%
China 36901
15.4%
Australia 25208
10.5%
Canada 15344
 
6.4%
United Kingdom 5480
 
2.3%

Length

2025-08-18T20:12:16.398152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:16.682456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united 62472
20.7%
brazil 59184
19.6%
states 56992
18.9%
india 40552
13.4%
china 36901
12.2%
australia 25208
8.3%
canada 15344
 
5.1%
kingdom 5480
 
1.8%

Most occurring characters

ValueCountFrequency (%)
a 290077
15.4%
i 229797
12.2%
t 201664
10.7%
n 160749
 
8.6%
d 123848
 
6.6%
e 119464
 
6.4%
r 84392
 
4.5%
l 84392
 
4.5%
s 82200
 
4.4%
U 62472
 
3.3%
Other values (13) 439866
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1878921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 290077
15.4%
i 229797
12.2%
t 201664
10.7%
n 160749
 
8.6%
d 123848
 
6.6%
e 119464
 
6.4%
r 84392
 
4.5%
l 84392
 
4.5%
s 82200
 
4.4%
U 62472
 
3.3%
Other values (13) 439866
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1878921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 290077
15.4%
i 229797
12.2%
t 201664
10.7%
n 160749
 
8.6%
d 123848
 
6.6%
e 119464
 
6.4%
r 84392
 
4.5%
l 84392
 
4.5%
s 82200
 
4.4%
U 62472
 
3.3%
Other values (13) 439866
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1878921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 290077
15.4%
i 229797
12.2%
t 201664
10.7%
n 160749
 
8.6%
d 123848
 
6.6%
e 119464
 
6.4%
r 84392
 
4.5%
l 84392
 
4.5%
s 82200
 
4.4%
U 62472
 
3.3%
Other values (13) 439866
23.4%

CountryCode
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.7 MiB
BRA
59184 
USA
56992 
IND
40552 
CHN
36901 
AUS
25208 
Other values (2)
20824 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters718,983
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAUS
2nd rowAUS
3rd rowAUS
4th rowAUS
5th rowAUS

Common Values

ValueCountFrequency (%)
BRA 59184
24.7%
USA 56992
23.8%
IND 40552
16.9%
CHN 36901
15.4%
AUS 25208
10.5%
CAN 15344
 
6.4%
GBR 5480
 
2.3%

Length

2025-08-18T20:12:17.032645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:17.352445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bra 59184
24.7%
usa 56992
23.8%
ind 40552
16.9%
chn 36901
15.4%
aus 25208
10.5%
can 15344
 
6.4%
gbr 5480
 
2.3%

Most occurring characters

ValueCountFrequency (%)
A 156728
21.8%
N 92797
12.9%
U 82200
11.4%
S 82200
11.4%
B 64664
9.0%
R 64664
9.0%
C 52245
 
7.3%
I 40552
 
5.6%
D 40552
 
5.6%
H 36901
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 718983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 156728
21.8%
N 92797
12.9%
U 82200
11.4%
S 82200
11.4%
B 64664
9.0%
R 64664
9.0%
C 52245
 
7.3%
I 40552
 
5.6%
D 40552
 
5.6%
H 36901
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 718983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 156728
21.8%
N 92797
12.9%
U 82200
11.4%
S 82200
11.4%
B 64664
9.0%
R 64664
9.0%
C 52245
 
7.3%
I 40552
 
5.6%
D 40552
 
5.6%
H 36901
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 718983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 156728
21.8%
N 92797
12.9%
U 82200
11.4%
S 82200
11.4%
B 64664
9.0%
R 64664
9.0%
C 52245
 
7.3%
I 40552
 
5.6%
D 40552
 
5.6%
H 36901
 
5.1%

RegionName
Text

Missing 

Distinct170
Distinct (%)0.1%
Missing7672
Missing (%)3.2%
Memory size15.0 MiB
2025-08-18T20:12:18.471540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length25
Mean length9.5986534
Min length3

Characters and Unicode

Total characters2,226,782
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralian Capital Territory
2nd rowAustralian Capital Territory
3rd rowAustralian Capital Territory
4th rowAustralian Capital Territory
5th rowAustralian Capital Territory
ValueCountFrequency (%)
new 8768
 
2.7%
south 8768
 
2.7%
do 6576
 
2.0%
rio 6576
 
2.0%
australia 6576
 
2.0%
and 6576
 
2.0%
pradesh 5480
 
1.7%
territory 4384
 
1.3%
grosso 4384
 
1.3%
sul 4384
 
1.3%
Other values (188) 268216
81.1%
2025-08-18T20:12:19.791993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 339102
15.2%
n 181067
 
8.1%
i 176270
 
7.9%
r 148019
 
6.6%
o 140760
 
6.3%
e 125475
 
5.6%
s 107526
 
4.8%
98699
 
4.4%
t 95411
 
4.3%
h 77369
 
3.5%
Other values (43) 737084
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2226782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 339102
15.2%
n 181067
 
8.1%
i 176270
 
7.9%
r 148019
 
6.6%
o 140760
 
6.3%
e 125475
 
5.6%
s 107526
 
4.8%
98699
 
4.4%
t 95411
 
4.3%
h 77369
 
3.5%
Other values (43) 737084
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2226782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 339102
15.2%
n 181067
 
8.1%
i 176270
 
7.9%
r 148019
 
6.6%
o 140760
 
6.3%
e 125475
 
5.6%
s 107526
 
4.8%
98699
 
4.4%
t 95411
 
4.3%
h 77369
 
3.5%
Other values (43) 737084
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2226782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 339102
15.2%
n 181067
 
8.1%
i 176270
 
7.9%
r 148019
 
6.6%
o 140760
 
6.3%
e 125475
 
5.6%
s 107526
 
4.8%
98699
 
4.4%
t 95411
 
4.3%
h 77369
 
3.5%
Other values (43) 737084
33.1%

RegionCode
Text

Missing 

Distinct170
Distinct (%)0.1%
Missing7672
Missing (%)3.2%
Memory size14.1 MiB
2025-08-18T20:12:21.020401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.5700831
Min length5

Characters and Unicode

Total characters1,292,198
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAUS_ACT
2nd rowAUS_ACT
3rd rowAUS_ACT
4th rowAUS_ACT
5th rowAUS_ACT
ValueCountFrequency (%)
aus_nsw 3288
 
1.4%
aus_nt 3288
 
1.4%
aus_vic 3288
 
1.4%
aus_qld 3288
 
1.4%
aus_sa 3288
 
1.4%
aus_tas 3288
 
1.4%
aus_wa 3288
 
1.4%
br_ac 2192
 
0.9%
br_al 2192
 
0.9%
br_am 2192
 
0.9%
Other values (160) 202397
87.2%
2025-08-18T20:12:22.569731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 231989
18.0%
N 130415
10.1%
S 120973
9.4%
A 89990
 
7.0%
U 88776
 
6.9%
R 84392
 
6.5%
C 75379
 
5.8%
B 72454
 
5.6%
D 57110
 
4.4%
I 55955
 
4.3%
Other values (17) 284765
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1292198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 231989
18.0%
N 130415
10.1%
S 120973
9.4%
A 89990
 
7.0%
U 88776
 
6.9%
R 84392
 
6.5%
C 75379
 
5.8%
B 72454
 
5.6%
D 57110
 
4.4%
I 55955
 
4.3%
Other values (17) 284765
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1292198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 231989
18.0%
N 130415
10.1%
S 120973
9.4%
A 89990
 
7.0%
U 88776
 
6.9%
R 84392
 
6.5%
C 75379
 
5.8%
B 72454
 
5.6%
D 57110
 
4.4%
I 55955
 
4.3%
Other values (17) 284765
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1292198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 231989
18.0%
N 130415
10.1%
S 120973
9.4%
A 89990
 
7.0%
U 88776
 
6.9%
R 84392
 
6.5%
C 75379
 
5.8%
B 72454
 
5.6%
D 57110
 
4.4%
I 55955
 
4.3%
Other values (17) 284765
22.0%

CityName
Categorical

High correlation  Missing  Uniform 

Distinct40
Distinct (%)0.1%
Missing195821
Missing (%)81.7%
Memory size14.9 MiB
Greater Sydney
 
1096
Rest of New South Wales
 
1096
Greater Darwin
 
1096
Rest of Northern Territory
 
1096
Greater Brisbane
 
1096
Other values (35)
38360 

Length

Max length26
Median length17
Mean length11.85
Min length5

Characters and Unicode

Total characters519,504
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreater Sydney
2nd rowGreater Sydney
3rd rowGreater Sydney
4th rowGreater Sydney
5th rowGreater Sydney

Common Values

ValueCountFrequency (%)
Greater Sydney 1096
 
0.5%
Rest of New South Wales 1096
 
0.5%
Greater Darwin 1096
 
0.5%
Rest of Northern Territory 1096
 
0.5%
Greater Brisbane 1096
 
0.5%
Rest of Queensland 1096
 
0.5%
Greater Adelaide 1096
 
0.5%
Rest of South Australia 1096
 
0.5%
Greater Hobart 1096
 
0.5%
Rest of Tasmania 1096
 
0.5%
Other values (30) 32880
 
13.7%
(Missing) 195821
81.7%

Length

2025-08-18T20:12:22.877648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
greater 7672
 
9.1%
rest 7672
 
9.1%
of 7672
 
9.1%
south 2192
 
2.6%
rio 2192
 
2.6%
australia 2192
 
2.6%
porto 2192
 
2.6%
sao 2192
 
2.6%
new 1096
 
1.3%
sydney 1096
 
1.3%
Other values (44) 48224
57.1%

Most occurring characters

ValueCountFrequency (%)
a 61376
11.8%
e 58088
11.2%
r 46032
 
8.9%
o 46032
 
8.9%
38360
 
7.4%
t 35072
 
6.8%
i 29592
 
5.7%
s 24112
 
4.6%
l 18632
 
3.6%
n 18632
 
3.6%
Other values (33) 143576
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 519504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 61376
11.8%
e 58088
11.2%
r 46032
 
8.9%
o 46032
 
8.9%
38360
 
7.4%
t 35072
 
6.8%
i 29592
 
5.7%
s 24112
 
4.6%
l 18632
 
3.6%
n 18632
 
3.6%
Other values (33) 143576
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 519504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 61376
11.8%
e 58088
11.2%
r 46032
 
8.9%
o 46032
 
8.9%
38360
 
7.4%
t 35072
 
6.8%
i 29592
 
5.7%
s 24112
 
4.6%
l 18632
 
3.6%
n 18632
 
3.6%
Other values (33) 143576
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 519504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 61376
11.8%
e 58088
11.2%
r 46032
 
8.9%
o 46032
 
8.9%
38360
 
7.4%
t 35072
 
6.8%
i 29592
 
5.7%
s 24112
 
4.6%
l 18632
 
3.6%
n 18632
 
3.6%
Other values (33) 143576
27.6%

CityCode
Categorical

High correlation  Missing  Uniform 

Distinct40
Distinct (%)0.1%
Missing195821
Missing (%)81.7%
Memory size14.7 MiB
AUS_1GSYD
 
1096
AUS_1RNSW
 
1096
AUS_7GDAR
 
1096
AUS_7RNTE
 
1096
AUS_3GBRI
 
1096
Other values (35)
38360 

Length

Max length10
Median length10
Mean length9.65
Min length9

Characters and Unicode

Total characters423,056
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAUS_1GSYD
2nd rowAUS_1GSYD
3rd rowAUS_1GSYD
4th rowAUS_1GSYD
5th rowAUS_1GSYD

Common Values

ValueCountFrequency (%)
AUS_1GSYD 1096
 
0.5%
AUS_1RNSW 1096
 
0.5%
AUS_7GDAR 1096
 
0.5%
AUS_7RNTE 1096
 
0.5%
AUS_3GBRI 1096
 
0.5%
AUS_3RQLD 1096
 
0.5%
AUS_4GADE 1096
 
0.5%
AUS_4RSAU 1096
 
0.5%
AUS_6GHOB 1096
 
0.5%
AUS_6RTAS 1096
 
0.5%
Other values (30) 32880
 
13.7%
(Missing) 195821
81.7%

Length

2025-08-18T20:12:23.256365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aus_1gsyd 1096
 
2.5%
aus_1rnsw 1096
 
2.5%
aus_7gdar 1096
 
2.5%
aus_7rnte 1096
 
2.5%
aus_3gbri 1096
 
2.5%
aus_3rqld 1096
 
2.5%
aus_4gade 1096
 
2.5%
aus_4rsau 1096
 
2.5%
aus_6ghob 1096
 
2.5%
aus_6rtas 1096
 
2.5%
Other values (30) 32880
75.0%

Most occurring characters

ValueCountFrequency (%)
0 63568
15.0%
_ 43840
10.4%
R 39456
 
9.3%
B 30688
 
7.3%
2 29592
 
7.0%
1 29592
 
7.0%
3 21920
 
5.2%
A 20824
 
4.9%
S 19728
 
4.7%
4 19728
 
4.7%
Other values (22) 104120
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 423056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 63568
15.0%
_ 43840
10.4%
R 39456
 
9.3%
B 30688
 
7.3%
2 29592
 
7.0%
1 29592
 
7.0%
3 21920
 
5.2%
A 20824
 
4.9%
S 19728
 
4.7%
4 19728
 
4.7%
Other values (22) 104120
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 423056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 63568
15.0%
_ 43840
10.4%
R 39456
 
9.3%
B 30688
 
7.3%
2 29592
 
7.0%
1 29592
 
7.0%
3 21920
 
5.2%
A 20824
 
4.9%
S 19728
 
4.7%
4 19728
 
4.7%
Other values (22) 104120
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 423056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 63568
15.0%
_ 43840
10.4%
R 39456
 
9.3%
B 30688
 
7.3%
2 29592
 
7.0%
1 29592
 
7.0%
3 21920
 
5.2%
A 20824
 
4.9%
S 19728
 
4.7%
4 19728
 
4.7%
Other values (22) 104120
24.6%

Jurisdiction
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.5 MiB
STATE_TOTAL
188149 
CITY_TOTAL
43840 
NAT_TOTAL
 
7672

Length

Max length11
Median length11
Mean length10.753051
Min length9

Characters and Unicode

Total characters2,577,087
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNAT_TOTAL
2nd rowNAT_TOTAL
3rd rowNAT_TOTAL
4th rowNAT_TOTAL
5th rowNAT_TOTAL

Common Values

ValueCountFrequency (%)
STATE_TOTAL 188149
78.5%
CITY_TOTAL 43840
 
18.3%
NAT_TOTAL 7672
 
3.2%

Length

2025-08-18T20:12:23.576507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:23.829361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
state_total 188149
78.5%
city_total 43840
 
18.3%
nat_total 7672
 
3.2%

Most occurring characters

ValueCountFrequency (%)
T 907132
35.2%
A 435482
16.9%
_ 239661
 
9.3%
L 239661
 
9.3%
O 239661
 
9.3%
S 188149
 
7.3%
E 188149
 
7.3%
C 43840
 
1.7%
I 43840
 
1.7%
Y 43840
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2577087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 907132
35.2%
A 435482
16.9%
_ 239661
 
9.3%
L 239661
 
9.3%
O 239661
 
9.3%
S 188149
 
7.3%
E 188149
 
7.3%
C 43840
 
1.7%
I 43840
 
1.7%
Y 43840
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2577087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 907132
35.2%
A 435482
16.9%
_ 239661
 
9.3%
L 239661
 
9.3%
O 239661
 
9.3%
S 188149
 
7.3%
E 188149
 
7.3%
C 43840
 
1.7%
I 43840
 
1.7%
Y 43840
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2577087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 907132
35.2%
A 435482
16.9%
_ 239661
 
9.3%
L 239661
 
9.3%
O 239661
 
9.3%
S 188149
 
7.3%
E 188149
 
7.3%
C 43840
 
1.7%
I 43840
 
1.7%
Y 43840
 
1.7%

Date
Real number (ℝ)

High correlation 

Distinct1155
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210808
Minimum20200101
Maximum20230228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-08-18T20:12:24.208028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20200101
5-th percentile20200225
Q120201003
median20210706
Q320220408
95-th percentile20221115
Maximum20230228
Range30127
Interquartile range (IQR)19405

Descriptive statistics

Standard deviation8318.3756
Coefficient of variation (CV)0.00041158056
Kurtosis-1.3819664
Mean20210808
Median Absolute Deviation (MAD)9703
Skewness0.046188878
Sum4.8437424 × 1012
Variance69195373
MonotonicityNot monotonic
2025-08-18T20:12:24.638298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20221231 217
 
0.1%
20221230 217
 
0.1%
20221229 217
 
0.1%
20221228 217
 
0.1%
20221227 217
 
0.1%
20221226 217
 
0.1%
20221225 217
 
0.1%
20221224 217
 
0.1%
20200201 217
 
0.1%
20200131 217
 
0.1%
Other values (1145) 237491
99.1%
ValueCountFrequency (%)
20200101 217
0.1%
20200102 217
0.1%
20200103 217
0.1%
20200104 217
0.1%
20200105 217
0.1%
20200106 217
0.1%
20200107 217
0.1%
20200108 217
0.1%
20200109 217
0.1%
20200110 217
0.1%
ValueCountFrequency (%)
20230228 31
< 0.1%
20230227 31
< 0.1%
20230226 31
< 0.1%
20230225 31
< 0.1%
20230224 31
< 0.1%
20230223 31
< 0.1%
20230222 31
< 0.1%
20230221 31
< 0.1%
20230220 31
< 0.1%
20230219 31
< 0.1%

C1M_School.closing
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
1
87670 
3
55918 
0
54711 
2
41362 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 87670
36.6%
3 55918
23.3%
0 54711
22.8%
2 41362
17.3%

Length

2025-08-18T20:12:25.036808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:25.405702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 87670
36.6%
3 55918
23.3%
0 54711
22.8%
2 41362
17.3%

Most occurring characters

ValueCountFrequency (%)
1 87670
36.6%
3 55918
23.3%
0 54711
22.8%
2 41362
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 87670
36.6%
3 55918
23.3%
0 54711
22.8%
2 41362
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 87670
36.6%
3 55918
23.3%
0 54711
22.8%
2 41362
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 87670
36.6%
3 55918
23.3%
0 54711
22.8%
2 41362
17.3%

C1M_Flag
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing54711
Missing (%)22.8%
Memory size13.9 MiB
1.0
144912 
0.0
40038 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters554,850
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 144912
60.5%
0.0 40038
 
16.7%
(Missing) 54711
 
22.8%

Length

2025-08-18T20:12:25.821209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:26.127905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 144912
78.4%
0.0 40038
 
21.6%

Most occurring characters

ValueCountFrequency (%)
0 224988
40.5%
. 184950
33.3%
1 144912
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 554850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 224988
40.5%
. 184950
33.3%
1 144912
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 554850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 224988
40.5%
. 184950
33.3%
1 144912
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 554850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 224988
40.5%
. 184950
33.3%
1 144912
26.1%

C2M_Workplace.closing
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
1
82006 
0
76485 
2
57893 
3
23277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 82006
34.2%
0 76485
31.9%
2 57893
24.2%
3 23277
 
9.7%

Length

2025-08-18T20:12:26.369831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:26.960293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 82006
34.2%
0 76485
31.9%
2 57893
24.2%
3 23277
 
9.7%

Most occurring characters

ValueCountFrequency (%)
1 82006
34.2%
0 76485
31.9%
2 57893
24.2%
3 23277
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 82006
34.2%
0 76485
31.9%
2 57893
24.2%
3 23277
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 82006
34.2%
0 76485
31.9%
2 57893
24.2%
3 23277
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 82006
34.2%
0 76485
31.9%
2 57893
24.2%
3 23277
 
9.7%

C2M_Flag
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing76485
Missing (%)31.9%
Memory size14.0 MiB
1.0
130738 
0.0
32438 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters489,528
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 130738
54.6%
0.0 32438
 
13.5%
(Missing) 76485
31.9%

Length

2025-08-18T20:12:27.272084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:27.511245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 130738
80.1%
0.0 32438
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 195614
40.0%
. 163176
33.3%
1 130738
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 489528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 195614
40.0%
. 163176
33.3%
1 130738
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 489528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 195614
40.0%
. 163176
33.3%
1 130738
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 489528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 195614
40.0%
. 163176
33.3%
1 130738
26.7%

C3M_Cancel.public.events
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2
82901 
1
80008 
0
76752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 82901
34.6%
1 80008
33.4%
0 76752
32.0%

Length

2025-08-18T20:12:27.792333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:28.002041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 82901
34.6%
1 80008
33.4%
0 76752
32.0%

Most occurring characters

ValueCountFrequency (%)
2 82901
34.6%
1 80008
33.4%
0 76752
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 82901
34.6%
1 80008
33.4%
0 76752
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 82901
34.6%
1 80008
33.4%
0 76752
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 82901
34.6%
1 80008
33.4%
0 76752
32.0%

C3M_Flag
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing76752
Missing (%)32.0%
Memory size14.0 MiB
1.0
134353 
0.0
28556 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters488,727
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 134353
56.1%
0.0 28556
 
11.9%
(Missing) 76752
32.0%

Length

2025-08-18T20:12:28.306992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:28.489032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 134353
82.5%
0.0 28556
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 191465
39.2%
. 162909
33.3%
1 134353
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 488727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 191465
39.2%
. 162909
33.3%
1 134353
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 488727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 191465
39.2%
. 162909
33.3%
1 134353
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 488727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 191465
39.2%
. 162909
33.3%
1 134353
27.5%

C4M_Restrictions.on.gatherings
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0
103505 
4
54154 
3
43655 
2
24409 
1
13938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 103505
43.2%
4 54154
22.6%
3 43655
18.2%
2 24409
 
10.2%
1 13938
 
5.8%

Length

2025-08-18T20:12:28.746556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:28.998507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 103505
43.2%
4 54154
22.6%
3 43655
18.2%
2 24409
 
10.2%
1 13938
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 103505
43.2%
4 54154
22.6%
3 43655
18.2%
2 24409
 
10.2%
1 13938
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103505
43.2%
4 54154
22.6%
3 43655
18.2%
2 24409
 
10.2%
1 13938
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103505
43.2%
4 54154
22.6%
3 43655
18.2%
2 24409
 
10.2%
1 13938
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103505
43.2%
4 54154
22.6%
3 43655
18.2%
2 24409
 
10.2%
1 13938
 
5.8%

C4M_Flag
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing103505
Missing (%)43.2%
Memory size14.1 MiB
1.0
105526 
0.0
30630 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters408,468
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 105526
44.0%
0.0 30630
 
12.8%
(Missing) 103505
43.2%

Length

2025-08-18T20:12:29.361734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:29.648581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 105526
77.5%
0.0 30630
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0 166786
40.8%
. 136156
33.3%
1 105526
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 408468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 166786
40.8%
. 136156
33.3%
1 105526
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 408468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 166786
40.8%
. 136156
33.3%
1 105526
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 408468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 166786
40.8%
. 136156
33.3%
1 105526
25.8%

C5M_Close.public.transport
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0
145190 
1
74354 
2
20117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 145190
60.6%
1 74354
31.0%
2 20117
 
8.4%

Length

2025-08-18T20:12:29.888351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:30.101415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 145190
60.6%
1 74354
31.0%
2 20117
 
8.4%

Most occurring characters

ValueCountFrequency (%)
0 145190
60.6%
1 74354
31.0%
2 20117
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 145190
60.6%
1 74354
31.0%
2 20117
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 145190
60.6%
1 74354
31.0%
2 20117
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 145190
60.6%
1 74354
31.0%
2 20117
 
8.4%

C5M_Flag
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing145190
Missing (%)60.6%
Memory size14.3 MiB
1.0
66455 
0.0
28016 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters283,413
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 66455
27.7%
0.0 28016
 
11.7%
(Missing) 145190
60.6%

Length

2025-08-18T20:12:30.418984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:30.650194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 66455
70.3%
0.0 28016
29.7%

Most occurring characters

ValueCountFrequency (%)
0 122487
43.2%
. 94471
33.3%
1 66455
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 283413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 122487
43.2%
. 94471
33.3%
1 66455
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 283413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 122487
43.2%
. 94471
33.3%
1 66455
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 283413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 122487
43.2%
. 94471
33.3%
1 66455
23.4%

C6M_Stay.at.home.requirements
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0
125301 
1
73916 
2
31798 
3
 
8646

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125301
52.3%
1 73916
30.8%
2 31798
 
13.3%
3 8646
 
3.6%

Length

2025-08-18T20:12:30.927811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:31.262672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 125301
52.3%
1 73916
30.8%
2 31798
 
13.3%
3 8646
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 125301
52.3%
1 73916
30.8%
2 31798
 
13.3%
3 8646
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 125301
52.3%
1 73916
30.8%
2 31798
 
13.3%
3 8646
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 125301
52.3%
1 73916
30.8%
2 31798
 
13.3%
3 8646
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 125301
52.3%
1 73916
30.8%
2 31798
 
13.3%
3 8646
 
3.6%

C6M_Flag
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing125301
Missing (%)52.3%
Memory size14.2 MiB
1.0
89381 
0.0
24979 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters343,080
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 89381
37.3%
0.0 24979
 
10.4%
(Missing) 125301
52.3%

Length

2025-08-18T20:12:31.600214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:31.810504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 89381
78.2%
0.0 24979
 
21.8%

Most occurring characters

ValueCountFrequency (%)
0 139339
40.6%
. 114360
33.3%
1 89381
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 343080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 139339
40.6%
. 114360
33.3%
1 89381
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 343080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 139339
40.6%
. 114360
33.3%
1 89381
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 343080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 139339
40.6%
. 114360
33.3%
1 89381
26.1%

C7M_Restrictions.on.internal.movement
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0
102801 
1
69269 
2
67591 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 102801
42.9%
1 69269
28.9%
2 67591
28.2%

Length

2025-08-18T20:12:32.117562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:32.394243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 102801
42.9%
1 69269
28.9%
2 67591
28.2%

Most occurring characters

ValueCountFrequency (%)
0 102801
42.9%
1 69269
28.9%
2 67591
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 102801
42.9%
1 69269
28.9%
2 67591
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 102801
42.9%
1 69269
28.9%
2 67591
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 102801
42.9%
1 69269
28.9%
2 67591
28.2%

C7M_Flag
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing102801
Missing (%)42.9%
Memory size14.1 MiB
1.0
112205 
0.0
24655 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters410,580
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 112205
46.8%
0.0 24655
 
10.3%
(Missing) 102801
42.9%

Length

2025-08-18T20:12:32.651395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:32.882240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 112205
82.0%
0.0 24655
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 161515
39.3%
. 136860
33.3%
1 112205
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 410580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 161515
39.3%
. 136860
33.3%
1 112205
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 410580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 161515
39.3%
. 136860
33.3%
1 112205
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 410580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 161515
39.3%
. 136860
33.3%
1 112205
27.3%

C8EV_International.travel.controls
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
3
98926 
1
74019 
4
41114 
0
16907 
2
 
8695

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
3 98926
41.3%
1 74019
30.9%
4 41114
17.2%
0 16907
 
7.1%
2 8695
 
3.6%

Length

2025-08-18T20:12:33.179863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:33.398369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 98926
41.3%
1 74019
30.9%
4 41114
17.2%
0 16907
 
7.1%
2 8695
 
3.6%

Most occurring characters

ValueCountFrequency (%)
3 98926
41.3%
1 74019
30.9%
4 41114
17.2%
0 16907
 
7.1%
2 8695
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 98926
41.3%
1 74019
30.9%
4 41114
17.2%
0 16907
 
7.1%
2 8695
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 98926
41.3%
1 74019
30.9%
4 41114
17.2%
0 16907
 
7.1%
2 8695
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 98926
41.3%
1 74019
30.9%
4 41114
17.2%
0 16907
 
7.1%
2 8695
 
3.6%

E1_Income.support
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
1
109613 
0
96993 
2
33055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 109613
45.7%
0 96993
40.5%
2 33055
 
13.8%

Length

2025-08-18T20:12:33.669240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:33.849217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 109613
45.7%
0 96993
40.5%
2 33055
 
13.8%

Most occurring characters

ValueCountFrequency (%)
1 109613
45.7%
0 96993
40.5%
2 33055
 
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 109613
45.7%
0 96993
40.5%
2 33055
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 109613
45.7%
0 96993
40.5%
2 33055
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 109613
45.7%
0 96993
40.5%
2 33055
 
13.8%

E1_Flag
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing96993
Missing (%)40.5%
Memory size14.1 MiB
0.0
86619 
1.0
56049 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters428,004
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 86619
36.1%
1.0 56049
23.4%
(Missing) 96993
40.5%

Length

2025-08-18T20:12:34.112907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:34.369393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 86619
60.7%
1.0 56049
39.3%

Most occurring characters

ValueCountFrequency (%)
0 229287
53.6%
. 142668
33.3%
1 56049
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 428004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 229287
53.6%
. 142668
33.3%
1 56049
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 428004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 229287
53.6%
. 142668
33.3%
1 56049
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 428004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 229287
53.6%
. 142668
33.3%
1 56049
 
13.1%

E2_Debt.contract.relief
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0
105315 
1
87737 
2
46609 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 105315
43.9%
1 87737
36.6%
2 46609
19.4%

Length

2025-08-18T20:12:34.615185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:34.811907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 105315
43.9%
1 87737
36.6%
2 46609
19.4%

Most occurring characters

ValueCountFrequency (%)
0 105315
43.9%
1 87737
36.6%
2 46609
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 105315
43.9%
1 87737
36.6%
2 46609
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 105315
43.9%
1 87737
36.6%
2 46609
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 105315
43.9%
1 87737
36.6%
2 46609
19.4%

E3_Fiscal.measures
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct430
Distinct (%)1.0%
Missing194695
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean2.6828084 × 108
Minimum-0.01
Maximum1.9576 × 1012
Zeros44491
Zeros (%)18.6%
Negative7
Negative (%)< 0.1%
Memory size1.8 MiB
2025-08-18T20:12:35.148669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.9576 × 1012
Range1.9576 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7225828 × 1010
Coefficient of variation (CV)64.208193
Kurtosis8971.3766
Mean2.6828084 × 108
Median Absolute Deviation (MAD)0
Skewness90.04819
Sum1.2063516 × 1013
Variance2.9672914 × 1020
MonotonicityNot monotonic
2025-08-18T20:12:35.597360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44491
 
18.6%
-0.01 7
 
< 0.1%
10000000 5
 
< 0.1%
50000000 5
 
< 0.1%
40000000 5
 
< 0.1%
200000000 5
 
< 0.1%
25000000 4
 
< 0.1%
20000000 4
 
< 0.1%
2000000 3
 
< 0.1%
4000000 3
 
< 0.1%
Other values (420) 434
 
0.2%
(Missing) 194695
81.2%
ValueCountFrequency (%)
-0.01 7
 
< 0.1%
0 44491
18.6%
43.25 1
 
< 0.1%
3885.19 1
 
< 0.1%
13895.75 1
 
< 0.1%
23076.84 1
 
< 0.1%
24963.76 1
 
< 0.1%
26000 1
 
< 0.1%
56092.43 1
 
< 0.1%
70000 1
 
< 0.1%
ValueCountFrequency (%)
1.9576 × 10121
< 0.1%
1.9 × 10121
< 0.1%
1.47737366 × 10121
< 0.1%
1.192572826 × 10121
< 0.1%
9 × 10111
< 0.1%
7.266578206 × 10111
< 0.1%
4.204525711 × 10111
< 0.1%
3.84 × 10111
< 0.1%
3.52 × 10111
< 0.1%
3.497990474 × 10111
< 0.1%

E4_International.support
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct43
Distinct (%)0.1%
Missing195213
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean167286.25
Minimum-0.01
Maximum1.4 × 109
Zeros44396
Zeros (%)18.5%
Negative7
Negative (%)< 0.1%
Memory size1.8 MiB
2025-08-18T20:12:36.002064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.4 × 109
Range1.4 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10626739
Coefficient of variation (CV)63.524287
Kurtosis12236.434
Mean167286.25
Median Absolute Deviation (MAD)0
Skewness102.36352
Sum7.435539 × 109
Variance1.1292759 × 1014
MonotonicityNot monotonic
2025-08-18T20:12:36.384399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 44396
 
18.5%
-0.01 7
 
< 0.1%
30000000 2
 
< 0.1%
186920130 2
 
< 0.1%
158429444 2
 
< 0.1%
9247426 2
 
< 0.1%
37827803 1
 
< 0.1%
6123664 1
 
< 0.1%
216550000 1
 
< 0.1%
364632406 1
 
< 0.1%
Other values (33) 33
 
< 0.1%
(Missing) 195213
81.5%
ValueCountFrequency (%)
-0.01 7
 
< 0.1%
0 44396
18.5%
19499.13 1
 
< 0.1%
57397.95 1
 
< 0.1%
215460 1
 
< 0.1%
250000 1
 
< 0.1%
322141.3 1
 
< 0.1%
331407 1
 
< 0.1%
352345 1
 
< 0.1%
423225 1
 
< 0.1%
ValueCountFrequency (%)
1400000000 1
< 0.1%
1300000000 1
< 0.1%
637249500 1
< 0.1%
380118265 1
< 0.1%
364632406 1
< 0.1%
308935500 1
< 0.1%
281340028 1
< 0.1%
274000000 1
< 0.1%
270000000 1
< 0.1%
225000000 1
< 0.1%

H1_Public.information.campaigns
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2
226800 
0
 
8924
1
 
3937

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 226800
94.6%
0 8924
 
3.7%
1 3937
 
1.6%

Length

2025-08-18T20:12:36.744651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:36.925749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 226800
94.6%
0 8924
 
3.7%
1 3937
 
1.6%

Most occurring characters

ValueCountFrequency (%)
2 226800
94.6%
0 8924
 
3.7%
1 3937
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 226800
94.6%
0 8924
 
3.7%
1 3937
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 226800
94.6%
0 8924
 
3.7%
1 3937
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 226800
94.6%
0 8924
 
3.7%
1 3937
 
1.6%

H1_Flag
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing8924
Missing (%)3.7%
Memory size13.7 MiB
1.0
229687 
0.0
 
1050

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters692,211
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 229687
95.8%
0.0 1050
 
0.4%
(Missing) 8924
 
3.7%

Length

2025-08-18T20:12:37.383733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:37.583273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 229687
99.5%
0.0 1050
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 231787
33.5%
. 230737
33.3%
1 229687
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 692211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 231787
33.5%
. 230737
33.3%
1 229687
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 692211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 231787
33.5%
. 230737
33.3%
1 229687
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 692211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 231787
33.5%
. 230737
33.3%
1 229687
33.2%

H2_Testing.policy
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
3
104615 
2
87448 
1
25332 
0
22266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
3 104615
43.7%
2 87448
36.5%
1 25332
 
10.6%
0 22266
 
9.3%

Length

2025-08-18T20:12:37.856356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:38.146210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 104615
43.7%
2 87448
36.5%
1 25332
 
10.6%
0 22266
 
9.3%

Most occurring characters

ValueCountFrequency (%)
3 104615
43.7%
2 87448
36.5%
1 25332
 
10.6%
0 22266
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 104615
43.7%
2 87448
36.5%
1 25332
 
10.6%
0 22266
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 104615
43.7%
2 87448
36.5%
1 25332
 
10.6%
0 22266
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 104615
43.7%
2 87448
36.5%
1 25332
 
10.6%
0 22266
 
9.3%

H3_Contact.tracing
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2
111723 
1
79354 
0
48584 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 111723
46.6%
1 79354
33.1%
0 48584
20.3%

Length

2025-08-18T20:12:38.432251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:38.646603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 111723
46.6%
1 79354
33.1%
0 48584
20.3%

Most occurring characters

ValueCountFrequency (%)
2 111723
46.6%
1 79354
33.1%
0 48584
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 111723
46.6%
1 79354
33.1%
0 48584
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 111723
46.6%
1 79354
33.1%
0 48584
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 111723
46.6%
1 79354
33.1%
0 48584
20.3%

H4_Emergency.investment.in.healthcare
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct269
Distinct (%)0.5%
Missing190612
Missing (%)79.5%
Infinite0
Infinite (%)0.0%
Mean13792584
Minimum-0.02
Maximum2.424 × 1011
Zeros48741
Zeros (%)20.3%
Negative31
Negative (%)< 0.1%
Memory size1.8 MiB
2025-08-18T20:12:39.106338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.424 × 1011
Range2.424 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3180628 × 109
Coefficient of variation (CV)95.56315
Kurtosis24975.444
Mean13792584
Median Absolute Deviation (MAD)0
Skewness148.78993
Sum6.7651248 × 1011
Variance1.7372896 × 1018
MonotonicityNot monotonic
2025-08-18T20:12:39.677369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48741
 
20.3%
-0.01 30
 
< 0.1%
1000000 4
 
< 0.1%
3764384670 3
 
< 0.1%
40000000 2
 
< 0.1%
30000000 2
 
< 0.1%
8000000 2
 
< 0.1%
1000000000 2
 
< 0.1%
200000000 2
 
< 0.1%
365208152.9 2
 
< 0.1%
Other values (259) 259
 
0.1%
(Missing) 190612
79.5%
ValueCountFrequency (%)
-0.02 1
 
< 0.1%
-0.01 30
 
< 0.1%
0 48741
20.3%
1 1
 
< 0.1%
14416.6 1
 
< 0.1%
91440 1
 
< 0.1%
134827 1
 
< 0.1%
141600 1
 
< 0.1%
168000 1
 
< 0.1%
172000 1
 
< 0.1%
ValueCountFrequency (%)
2.424 × 10111
< 0.1%
1.075199101 × 10111
< 0.1%
1 × 10111
< 0.1%
5.7458 × 10101
< 0.1%
3.236162967 × 10101
< 0.1%
1.419 × 10101
< 0.1%
6700000000 1
< 0.1%
6444663713 1
< 0.1%
5766077778 1
< 0.1%
5037940435 1
< 0.1%

H5_Investment.in.vaccines
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct87
Distinct (%)0.1%
Missing135859
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean1650843.1
Minimum-0.02
Maximum1.0040462 × 1011
Zeros103461
Zeros (%)43.2%
Negative251
Negative (%)0.1%
Memory size1.8 MiB
2025-08-18T20:12:40.187060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.0040462 × 1011
Range1.0040462 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2015253 × 108
Coefficient of variation (CV)193.93274
Kurtosis93274.754
Mean1650843.1
Median Absolute Deviation (MAD)0
Skewness299.15364
Sum1.7136082 × 1011
Variance1.0249764 × 1017
MonotonicityNot monotonic
2025-08-18T20:12:40.739911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103461
43.2%
-0.01 149
 
0.1%
-0.02 102
 
< 0.1%
76309000 3
 
< 0.1%
254591132.1 2
 
< 0.1%
10000000 2
 
< 0.1%
2989616.7 2
 
< 0.1%
77.53 2
 
< 0.1%
38778900 1
 
< 0.1%
127000000 1
 
< 0.1%
Other values (77) 77
 
< 0.1%
(Missing) 135859
56.7%
ValueCountFrequency (%)
-0.02 102
 
< 0.1%
-0.01 149
 
0.1%
0 103461
43.2%
77.53 2
 
< 0.1%
273000 1
 
< 0.1%
404899 1
 
< 0.1%
412087 1
 
< 0.1%
551724 1
 
< 0.1%
1000000 1
 
< 0.1%
1244635.19 1
 
< 0.1%
ValueCountFrequency (%)
1.004046156 × 10111
< 0.1%
1.575 × 10101
< 0.1%
1.229226111 × 10101
< 0.1%
9000000000 1
< 0.1%
6392520000 1
< 0.1%
3257700000 1
< 0.1%
1965940000 1
< 0.1%
1950000000 1
< 0.1%
1679740000 1
< 0.1%
1600000000 1
< 0.1%

H6M_Facial.Coverings
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2
83213 
4
58426 
3
50082 
0
24127 
1
23813 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 83213
34.7%
4 58426
24.4%
3 50082
20.9%
0 24127
 
10.1%
1 23813
 
9.9%

Length

2025-08-18T20:12:41.163320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:41.417439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 83213
34.7%
4 58426
24.4%
3 50082
20.9%
0 24127
 
10.1%
1 23813
 
9.9%

Most occurring characters

ValueCountFrequency (%)
2 83213
34.7%
4 58426
24.4%
3 50082
20.9%
0 24127
 
10.1%
1 23813
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 83213
34.7%
4 58426
24.4%
3 50082
20.9%
0 24127
 
10.1%
1 23813
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 83213
34.7%
4 58426
24.4%
3 50082
20.9%
0 24127
 
10.1%
1 23813
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 83213
34.7%
4 58426
24.4%
3 50082
20.9%
0 24127
 
10.1%
1 23813
 
9.9%

H6M_Flag
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing24127
Missing (%)10.1%
Memory size13.8 MiB
1.0
191852 
0.0
23682 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters646,602
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 191852
80.1%
0.0 23682
 
9.9%
(Missing) 24127
 
10.1%

Length

2025-08-18T20:12:42.207227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:42.377454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 191852
89.0%
0.0 23682
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 239216
37.0%
. 215534
33.3%
1 191852
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 646602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 239216
37.0%
. 215534
33.3%
1 191852
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 646602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 239216
37.0%
. 215534
33.3%
1 191852
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 646602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 239216
37.0%
. 215534
33.3%
1 191852
29.7%

H7_Vaccination.policy
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0344236
Minimum0
Maximum5
Zeros80733
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-08-18T20:12:42.548626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.2925996
Coefficient of variation (CV)0.75553049
Kurtosis-1.6723872
Mean3.0344236
Median Absolute Deviation (MAD)0
Skewness-0.46098021
Sum727233
Variance5.2560127
MonotonicityNot monotonic
2025-08-18T20:12:42.821384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 124965
52.1%
0 80733
33.7%
4 13401
 
5.6%
3 11280
 
4.7%
2 5682
 
2.4%
1 3600
 
1.5%
ValueCountFrequency (%)
0 80733
33.7%
1 3600
 
1.5%
2 5682
 
2.4%
3 11280
 
4.7%
4 13401
 
5.6%
5 124965
52.1%
ValueCountFrequency (%)
5 124965
52.1%
4 13401
 
5.6%
3 11280
 
4.7%
2 5682
 
2.4%
1 3600
 
1.5%
0 80733
33.7%

H7_Flag
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing98761
Missing (%)41.2%
Memory size14.1 MiB
1.0
140724 
0.0
 
176

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters422,700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 140724
58.7%
0.0 176
 
0.1%
(Missing) 98761
41.2%

Length

2025-08-18T20:12:43.127438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:43.337075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 140724
99.9%
0.0 176
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 141076
33.4%
. 140900
33.3%
1 140724
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 422700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 141076
33.4%
. 140900
33.3%
1 140724
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 422700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 141076
33.4%
. 140900
33.3%
1 140724
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 422700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 141076
33.4%
. 140900
33.3%
1 140724
33.3%

H8M_Protection.of.elderly.people
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2
87311 
1
64535 
3
46597 
0
41218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 87311
36.4%
1 64535
26.9%
3 46597
19.4%
0 41218
17.2%

Length

2025-08-18T20:12:43.546810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:43.743276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 87311
36.4%
1 64535
26.9%
3 46597
19.4%
0 41218
17.2%

Most occurring characters

ValueCountFrequency (%)
2 87311
36.4%
1 64535
26.9%
3 46597
19.4%
0 41218
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 87311
36.4%
1 64535
26.9%
3 46597
19.4%
0 41218
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 87311
36.4%
1 64535
26.9%
3 46597
19.4%
0 41218
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 87311
36.4%
1 64535
26.9%
3 46597
19.4%
0 41218
17.2%

H8M_Flag
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing41218
Missing (%)17.2%
Memory size13.9 MiB
1.0
178356 
0.0
20087 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters595,329
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 178356
74.4%
0.0 20087
 
8.4%
(Missing) 41218
 
17.2%

Length

2025-08-18T20:12:44.041854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:44.240106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 178356
89.9%
0.0 20087
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 218530
36.7%
. 198443
33.3%
1 178356
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 595329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 218530
36.7%
. 198443
33.3%
1 178356
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 595329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 218530
36.7%
. 198443
33.3%
1 178356
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 595329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 218530
36.7%
. 198443
33.3%
1 178356
30.0%

V1_Vaccine.Prioritisation..summary.
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2
100161 
0
83959 
1
55541 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 100161
41.8%
0 83959
35.0%
1 55541
23.2%

Length

2025-08-18T20:12:44.481203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:44.688627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 100161
41.8%
0 83959
35.0%
1 55541
23.2%

Most occurring characters

ValueCountFrequency (%)
2 100161
41.8%
0 83959
35.0%
1 55541
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 100161
41.8%
0 83959
35.0%
1 55541
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 100161
41.8%
0 83959
35.0%
1 55541
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 100161
41.8%
0 83959
35.0%
1 55541
23.2%

V2A_Vaccine.Availability..summary.
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
0
92013 
3
90009 
1
31808 
2
25831 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92013
38.4%
3 90009
37.6%
1 31808
 
13.3%
2 25831
 
10.8%

Length

2025-08-18T20:12:44.985207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:45.234175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 92013
38.4%
3 90009
37.6%
1 31808
 
13.3%
2 25831
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 92013
38.4%
3 90009
37.6%
1 31808
 
13.3%
2 25831
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 92013
38.4%
3 90009
37.6%
1 31808
 
13.3%
2 25831
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 92013
38.4%
3 90009
37.6%
1 31808
 
13.3%
2 25831
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 92013
38.4%
3 90009
37.6%
1 31808
 
13.3%
2 25831
 
10.8%

V2B_Vaccine.age.eligibility.availability.age.floor..general.population.summary.
Categorical

High correlation  Missing 

Distinct16
Distinct (%)< 0.1%
Missing100329
Missing (%)41.9%
Memory size14.8 MiB
5-15 yrs
57213 
0-4 yrs
34769 
16-19 yrs
26585 
60-64 yrs
 
4161
65-69 yrs
 
3205
Other values (11)
13399 

Length

Max length9
Median length8
Mean length8.0694098
Min length7

Characters and Unicode

Total characters1,124,327
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70-74 yrs
2nd row70-74 yrs
3rd row70-74 yrs
4th row70-74 yrs
5th row70-74 yrs

Common Values

ValueCountFrequency (%)
5-15 yrs 57213
23.9%
0-4 yrs 34769
 
14.5%
16-19 yrs 26585
 
11.1%
60-64 yrs 4161
 
1.7%
65-69 yrs 3205
 
1.3%
45-49 yrs 1889
 
0.8%
20-24 yrs 1739
 
0.7%
70-74 yrs 1577
 
0.7%
80+ yrs 1455
 
0.6%
50-54 yrs 1346
 
0.6%
Other values (6) 5393
 
2.3%
(Missing) 100329
41.9%

Length

2025-08-18T20:12:45.554977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
yrs 139332
50.0%
5-15 57213
20.5%
0-4 34769
 
12.5%
16-19 26585
 
9.5%
60-64 4161
 
1.5%
65-69 3205
 
1.2%
45-49 1889
 
0.7%
20-24 1739
 
0.6%
70-74 1577
 
0.6%
80 1455
 
0.5%
Other values (7) 6739
 
2.4%

Most occurring characters

ValueCountFrequency (%)
139332
12.4%
s 139332
12.4%
r 139332
12.4%
y 139332
12.4%
- 137877
12.3%
5 127734
11.4%
1 110383
9.8%
4 51763
 
4.6%
0 47088
 
4.2%
6 41317
 
3.7%
Other values (6) 50837
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1124327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
139332
12.4%
s 139332
12.4%
r 139332
12.4%
y 139332
12.4%
- 137877
12.3%
5 127734
11.4%
1 110383
9.8%
4 51763
 
4.6%
0 47088
 
4.2%
6 41317
 
3.7%
Other values (6) 50837
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1124327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
139332
12.4%
s 139332
12.4%
r 139332
12.4%
y 139332
12.4%
- 137877
12.3%
5 127734
11.4%
1 110383
9.8%
4 51763
 
4.6%
0 47088
 
4.2%
6 41317
 
3.7%
Other values (6) 50837
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1124327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
139332
12.4%
s 139332
12.4%
r 139332
12.4%
y 139332
12.4%
- 137877
12.3%
5 127734
11.4%
1 110383
9.8%
4 51763
 
4.6%
0 47088
 
4.2%
6 41317
 
3.7%
Other values (6) 50837
 
4.5%

V2C_Vaccine.age.eligibility.availability.age.floor..at.risk.summary.
Categorical

High correlation  Missing 

Distinct16
Distinct (%)< 0.1%
Missing100072
Missing (%)41.8%
Memory size14.8 MiB
5-15 yrs
57213 
0-4 yrs
34769 
16-19 yrs
31347 
60-64 yrs
 
2750
45-49 yrs
 
2472
Other values (11)
11038 

Length

Max length9
Median length8
Mean length8.0718825
Min length7

Characters and Unicode

Total characters1,126,746
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70-74 yrs
2nd row70-74 yrs
3rd row70-74 yrs
4th row70-74 yrs
5th row70-74 yrs

Common Values

ValueCountFrequency (%)
5-15 yrs 57213
23.9%
0-4 yrs 34769
 
14.5%
16-19 yrs 31347
 
13.1%
60-64 yrs 2750
 
1.1%
45-49 yrs 2472
 
1.0%
65-69 yrs 2276
 
0.9%
80+ yrs 1402
 
0.6%
20-24 yrs 1387
 
0.6%
75-79 yrs 1244
 
0.5%
70-74 yrs 1170
 
0.5%
Other values (6) 3559
 
1.5%
(Missing) 100072
41.8%

Length

2025-08-18T20:12:45.975636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
yrs 139589
50.0%
5-15 57213
20.5%
0-4 34769
 
12.5%
16-19 31347
 
11.2%
60-64 2750
 
1.0%
45-49 2472
 
0.9%
65-69 2276
 
0.8%
80 1402
 
0.5%
20-24 1387
 
0.5%
75-79 1244
 
0.4%
Other values (7) 4729
 
1.7%

Most occurring characters

ValueCountFrequency (%)
139589
12.4%
s 139589
12.4%
r 139589
12.4%
y 139589
12.4%
- 138187
12.3%
5 125671
11.2%
1 119907
10.6%
4 48142
 
4.3%
0 43492
 
3.9%
6 41399
 
3.7%
Other values (6) 51592
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1126746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
139589
12.4%
s 139589
12.4%
r 139589
12.4%
y 139589
12.4%
- 138187
12.3%
5 125671
11.2%
1 119907
10.6%
4 48142
 
4.3%
0 43492
 
3.9%
6 41399
 
3.7%
Other values (6) 51592
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1126746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
139589
12.4%
s 139589
12.4%
r 139589
12.4%
y 139589
12.4%
- 138187
12.3%
5 125671
11.2%
1 119907
10.6%
4 48142
 
4.3%
0 43492
 
3.9%
6 41399
 
3.7%
Other values (6) 51592
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1126746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
139589
12.4%
s 139589
12.4%
r 139589
12.4%
y 139589
12.4%
- 138187
12.3%
5 125671
11.2%
1 119907
10.6%
4 48142
 
4.3%
0 43492
 
3.9%
6 41399
 
3.7%
Other values (6) 51592
 
4.6%

V2D_Medically..clinically.vulnerable..Non.elderly.
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing91773
Missing (%)38.3%
Memory size14.1 MiB
2.0
117984 
1.0
15104 
0.0
14800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters443,664
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.0 117984
49.2%
1.0 15104
 
6.3%
0.0 14800
 
6.2%
(Missing) 91773
38.3%

Length

2025-08-18T20:12:46.338757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:46.567549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 117984
79.8%
1.0 15104
 
10.2%
0.0 14800
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 162688
36.7%
. 147888
33.3%
2 117984
26.6%
1 15104
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 162688
36.7%
. 147888
33.3%
2 117984
26.6%
1 15104
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 162688
36.7%
. 147888
33.3%
2 117984
26.6%
1 15104
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 162688
36.7%
. 147888
33.3%
2 117984
26.6%
1 15104
 
3.4%

V2E_Education
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing91773
Missing (%)38.3%
Memory size14.1 MiB
2.0
116249 
0.0
22813 
1.0
 
8826

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters443,664
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.0 116249
48.5%
0.0 22813
 
9.5%
1.0 8826
 
3.7%
(Missing) 91773
38.3%

Length

2025-08-18T20:12:46.881014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:47.303677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 116249
78.6%
0.0 22813
 
15.4%
1.0 8826
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 170701
38.5%
. 147888
33.3%
2 116249
26.2%
1 8826
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 170701
38.5%
. 147888
33.3%
2 116249
26.2%
1 8826
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 170701
38.5%
. 147888
33.3%
2 116249
26.2%
1 8826
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 170701
38.5%
. 147888
33.3%
2 116249
26.2%
1 8826
 
2.0%

V2F_Frontline.workers...non.healthcare.
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing91773
Missing (%)38.3%
Memory size14.1 MiB
2.0
129678 
0.0
 
10388
1.0
 
7822

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters443,664
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 129678
54.1%
0.0 10388
 
4.3%
1.0 7822
 
3.3%
(Missing) 91773
38.3%

Length

2025-08-18T20:12:47.610571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:47.815722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 129678
87.7%
0.0 10388
 
7.0%
1.0 7822
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 158276
35.7%
. 147888
33.3%
2 129678
29.2%
1 7822
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 158276
35.7%
. 147888
33.3%
2 129678
29.2%
1 7822
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 158276
35.7%
. 147888
33.3%
2 129678
29.2%
1 7822
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 158276
35.7%
. 147888
33.3%
2 129678
29.2%
1 7822
 
1.8%

V2G_Frontline.workers...healthcare.
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing91773
Missing (%)38.3%
Memory size14.1 MiB
2.0
131946 
1.0
14537 
0.0
 
1405

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters443,664
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 131946
55.1%
1.0 14537
 
6.1%
0.0 1405
 
0.6%
(Missing) 91773
38.3%

Length

2025-08-18T20:12:48.086945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:48.406175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 131946
89.2%
1.0 14537
 
9.8%
0.0 1405
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 149293
33.7%
. 147888
33.3%
2 131946
29.7%
1 14537
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 149293
33.7%
. 147888
33.3%
2 131946
29.7%
1 14537
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 149293
33.7%
. 147888
33.3%
2 131946
29.7%
1 14537
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 443664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 149293
33.7%
. 147888
33.3%
2 131946
29.7%
1 14537
 
3.3%

V3_Vaccine.Financial.Support..summary.
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
5
144074 
0
80220 
1
 
11851
4
 
2423
3
 
1093

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters239,661
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
5 144074
60.1%
0 80220
33.5%
1 11851
 
4.9%
4 2423
 
1.0%
3 1093
 
0.5%

Length

2025-08-18T20:12:48.653062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:48.878336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 144074
60.1%
0 80220
33.5%
1 11851
 
4.9%
4 2423
 
1.0%
3 1093
 
0.5%

Most occurring characters

ValueCountFrequency (%)
5 144074
60.1%
0 80220
33.5%
1 11851
 
4.9%
4 2423
 
1.0%
3 1093
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 144074
60.1%
0 80220
33.5%
1 11851
 
4.9%
4 2423
 
1.0%
3 1093
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 144074
60.1%
0 80220
33.5%
1 11851
 
4.9%
4 2423
 
1.0%
3 1093
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 144074
60.1%
0 80220
33.5%
1 11851
 
4.9%
4 2423
 
1.0%
3 1093
 
0.5%

V4_Mandatory.Vaccination..summary.
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing3114
Missing (%)1.3%
Memory size13.7 MiB
0.0
194642 
1.0
41905 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters709,641
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 194642
81.2%
1.0 41905
 
17.5%
(Missing) 3114
 
1.3%

Length

2025-08-18T20:12:49.255587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:49.470418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 194642
82.3%
1.0 41905
 
17.7%

Most occurring characters

ValueCountFrequency (%)
0 431189
60.8%
. 236547
33.3%
1 41905
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 709641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 431189
60.8%
. 236547
33.3%
1 41905
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 709641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 431189
60.8%
. 236547
33.3%
1 41905
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 709641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 431189
60.8%
. 236547
33.3%
1 41905
 
5.9%

ConfirmedCases
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct120667
Distinct (%)53.8%
Missing15569
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean969730.58
Minimum0
Maximum1.0076533 × 108
Zeros12826
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-08-18T20:12:49.803434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11753
median95463.5
Q3512790.25
95-th percentile2903628.7
Maximum1.0076533 × 108
Range1.0076533 × 108
Interquartile range (IQR)511037.25

Descriptive statistics

Standard deviation4824542.7
Coefficient of variation (CV)4.9751372
Kurtosis198.39453
Mean969730.58
Median Absolute Deviation (MAD)95316.5
Skewness12.608564
Sum2.1730887 × 1011
Variance2.3276212 × 1013
MonotonicityNot monotonic
2025-08-18T20:12:50.269214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12826
 
5.4%
1 1569
 
0.7%
18 693
 
0.3%
147 612
 
0.3%
75 483
 
0.2%
2 426
 
0.2%
980 344
 
0.1%
122 336
 
0.1%
171 336
 
0.1%
5 324
 
0.1%
Other values (120657) 206143
86.0%
(Missing) 15569
 
6.5%
ValueCountFrequency (%)
0 12826
5.4%
1 1569
 
0.7%
2 426
 
0.2%
3 231
 
0.1%
4 209
 
0.1%
5 324
 
0.1%
6 124
 
0.1%
7 166
 
0.1%
8 114
 
< 0.1%
9 114
 
< 0.1%
ValueCountFrequency (%)
100765333 1
< 0.1%
100757380 1
< 0.1%
100718983 1
< 0.1%
100614880 1
< 0.1%
100501536 1
< 0.1%
100390601 1
< 0.1%
100378169 1
< 0.1%
100374955 1
< 0.1%
100368433 1
< 0.1%
100329204 1
< 0.1%

ConfirmedDeaths
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct35386
Distinct (%)15.8%
Missing15859
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean14648.263
Minimum0
Maximum1092764
Zeros24940
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-08-18T20:12:50.813107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median1622.5
Q38477.75
95-th percentile42762.85
Maximum1092764
Range1092764
Interquartile range (IQR)8469.75

Descriptive statistics

Standard deviation66394.017
Coefficient of variation (CV)4.5325523
Kurtosis122.36206
Mean14648.263
Median Absolute Deviation (MAD)1622.5
Skewness10.232027
Sum3.2783105 × 109
Variance4.4081655 × 109
MonotonicityNot monotonic
2025-08-18T20:12:51.295797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24940
 
10.4%
2 6922
 
2.9%
1 5966
 
2.5%
3 5827
 
2.4%
6 4333
 
1.8%
7 3682
 
1.5%
4 3260
 
1.4%
9 1905
 
0.8%
13 1883
 
0.8%
8 1448
 
0.6%
Other values (35376) 163636
68.3%
(Missing) 15859
 
6.6%
ValueCountFrequency (%)
0 24940
10.4%
1 5966
 
2.5%
2 6922
 
2.9%
3 5827
 
2.4%
4 3260
 
1.4%
5 860
 
0.4%
6 4333
 
1.8%
7 3682
 
1.5%
8 1448
 
0.6%
9 1905
 
0.8%
ValueCountFrequency (%)
1092764 1
< 0.1%
1092738 1
< 0.1%
1092522 1
< 0.1%
1091598 1
< 0.1%
1090608 1
< 0.1%
1090252 1
< 0.1%
1090223 1
< 0.1%
1090208 1
< 0.1%
1090186 1
< 0.1%
1089979 1
< 0.1%

MajorityVaccinated
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.4 MiB
NV
138997 
V
100664 

Length

Max length2
Median length2
Mean length1.5799734
Min length1

Characters and Unicode

Total characters378,658
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNV
2nd rowNV
3rd rowNV
4th rowNV
5th rowNV

Common Values

ValueCountFrequency (%)
NV 138997
58.0%
V 100664
42.0%

Length

2025-08-18T20:12:51.679445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T20:12:51.900044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
nv 138997
58.0%
v 100664
42.0%

Most occurring characters

ValueCountFrequency (%)
V 239661
63.3%
N 138997
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 378658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 239661
63.3%
N 138997
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 378658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 239661
63.3%
N 138997
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 378658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 239661
63.3%
N 138997
36.7%

PopulationVaccinated
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size11.3 MiB

StringencyIndex_Average
Real number (ℝ)

High correlation  Zeros 

Distinct4405
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.645058
Minimum0
Maximum100
Zeros6168
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-08-18T20:12:52.239043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.11
Q123.25
median48.15
Q363.89
95-th percentile84.26
Maximum100
Range100
Interquartile range (IQR)40.64

Descriptive statistics

Standard deviation24.250225
Coefficient of variation (CV)0.53127821
Kurtosis-0.9854316
Mean45.645058
Median Absolute Deviation (MAD)20.37
Skewness0.018467404
Sum10939340
Variance588.07341
MonotonicityNot monotonic
2025-08-18T20:12:52.729453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.89 15182
 
6.3%
0 6168
 
2.6%
11.11 4541
 
1.9%
57.41 3988
 
1.7%
60.19 3848
 
1.6%
54.63 3767
 
1.6%
51.85 3762
 
1.6%
17.59 3614
 
1.5%
58.33 3521
 
1.5%
52.78 3024
 
1.3%
Other values (4395) 188246
78.5%
ValueCountFrequency (%)
0 6168
2.6%
2.78 1085
 
0.5%
5.56 2458
 
1.0%
7.41 1
 
< 0.1%
8.33 2132
 
0.9%
9.04 1
 
< 0.1%
9.5 14
 
< 0.1%
9.85 1
 
< 0.1%
10.18 14
 
< 0.1%
10.19 39
 
< 0.1%
ValueCountFrequency (%)
100 1388
0.6%
98.15 19
 
< 0.1%
97.22 768
0.3%
96.76 5
 
< 0.1%
96.3 92
 
< 0.1%
95.37 31
 
< 0.1%
94.44 465
 
0.2%
93.98 9
 
< 0.1%
93.52 427
 
0.2%
92.59 246
 
0.1%

GovernmentResponseIndex_Average
Real number (ℝ)

High correlation  Zeros 

Distinct4187
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.813978
Minimum0
Maximum92.19
Zeros5698
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-08-18T20:12:53.146226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.46
Q136.51
median51.93
Q362.5
95-th percentile75.52
Maximum92.19
Range92.19
Interquartile range (IQR)25.99

Descriptive statistics

Standard deviation18.816067
Coefficient of variation (CV)0.38546472
Kurtosis0.012414309
Mean48.813978
Median Absolute Deviation (MAD)12.65
Skewness-0.56275933
Sum11698807
Variance354.04436
MonotonicityNot monotonic
2025-08-18T20:12:53.603472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5698
 
2.4%
28.12 3435
 
1.4%
31.25 2926
 
1.2%
60.94 2248
 
0.9%
62.5 2148
 
0.9%
56.25 2080
 
0.9%
59.38 1946
 
0.8%
64.06 1884
 
0.8%
57.81 1859
 
0.8%
64.58 1761
 
0.7%
Other values (4177) 213676
89.2%
ValueCountFrequency (%)
0 5698
2.4%
1.56 620
 
0.3%
2.08 87
 
< 0.1%
3.12 1024
 
0.4%
4.69 1131
 
0.5%
5.21 431
 
0.2%
5.99 4
 
< 0.1%
6.25 869
 
0.4%
6.77 468
 
0.2%
7.81 115
 
< 0.1%
ValueCountFrequency (%)
92.19 3
 
< 0.1%
91.67 24
 
< 0.1%
91.46 24
 
< 0.1%
91.15 14
 
< 0.1%
90.94 12
 
< 0.1%
90.62 75
< 0.1%
90.42 30
 
< 0.1%
90.1 46
< 0.1%
89.84 11
 
< 0.1%
89.58 16
 
< 0.1%

ContainmentHealthIndex_Average
Real number (ℝ)

High correlation  Zeros 

Distinct4220
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.117583
Minimum0
Maximum98.21
Zeros5747
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-08-18T20:12:54.060605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.1
Q138.96
median54.17
Q364.88
95-th percentile79.17
Maximum98.21
Range98.21
Interquartile range (IQR)25.92

Descriptive statistics

Standard deviation19.268029
Coefficient of variation (CV)0.37693545
Kurtosis0.12898003
Mean51.117583
Median Absolute Deviation (MAD)12.61
Skewness-0.589306
Sum12250891
Variance371.25696
MonotonicityNot monotonic
2025-08-18T20:12:54.557816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.14 6359
 
2.7%
0 5747
 
2.4%
64.29 2663
 
1.1%
60.71 2469
 
1.0%
59.52 2333
 
1.0%
55.95 2287
 
1.0%
55.36 2138
 
0.9%
65.48 2090
 
0.9%
61.9 2057
 
0.9%
64.88 2028
 
0.8%
Other values (4210) 209490
87.4%
ValueCountFrequency (%)
0 5747
2.4%
1.79 610
 
0.3%
2.38 87
 
< 0.1%
3.57 1002
 
0.4%
5.36 1151
 
0.5%
5.95 434
 
0.2%
6.85 4
 
< 0.1%
7.14 837
 
0.3%
7.74 468
 
0.2%
8.93 116
 
< 0.1%
ValueCountFrequency (%)
98.21 7
 
< 0.1%
97.02 5
 
< 0.1%
96.43 14
 
< 0.1%
95.83 29
 
< 0.1%
95 2
 
< 0.1%
94.64 95
< 0.1%
94.05 45
< 0.1%
93.81 24
 
< 0.1%
93.45 14
 
< 0.1%
93.21 33
 
< 0.1%

EconomicSupportIndex
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.688725
Minimum0
Maximum100
Zeros62031
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-08-18T20:12:54.892056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q350
95-th percentile75
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation27.83342
Coefficient of variation (CV)0.85146851
Kurtosis-0.84776325
Mean32.688725
Median Absolute Deviation (MAD)25
Skewness0.45649843
Sum7834212.5
Variance774.69927
MonotonicityNot monotonic
2025-08-18T20:12:55.677148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 62031
25.9%
25 39790
16.6%
37.5 30822
12.9%
50 26707
11.1%
12.5 25415
10.6%
62.5 22266
 
9.3%
75 20903
 
8.7%
87.5 8116
 
3.4%
100 3611
 
1.5%
ValueCountFrequency (%)
0 62031
25.9%
12.5 25415
10.6%
25 39790
16.6%
37.5 30822
12.9%
50 26707
11.1%
62.5 22266
 
9.3%
75 20903
 
8.7%
87.5 8116
 
3.4%
100 3611
 
1.5%
ValueCountFrequency (%)
100 3611
 
1.5%
87.5 8116
 
3.4%
75 20903
 
8.7%
62.5 22266
 
9.3%
50 26707
11.1%
37.5 30822
12.9%
25 39790
16.6%
12.5 25415
10.6%
0 62031
25.9%

Interactions

2025-08-18T20:11:56.116123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:00.661948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:06.379259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:11.591335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:16.029361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:20.423793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:25.714334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:30.933133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:36.024190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:40.443074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:45.056465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:51.357346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:56.454920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:01.059656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:06.782885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:11.947834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:16.374386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:20.780671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:26.590936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:31.283583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:36.370016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:40.787617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:45.382394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:51.768378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:56.804127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:01.595673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:07.368487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:12.355631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:16.690752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:21.223073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:26.962674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:31.682538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:36.754095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:41.133711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:45.780046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:52.248446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:57.172782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:01.952263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:07.742816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:12.694715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:17.057287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:21.684764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:27.341226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:32.179921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:37.113068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:41.530207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:46.263651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:52.591036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:57.619166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:02.474535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:08.101715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:13.060910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:17.475930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:22.306196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:27.676597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:32.577277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:37.467292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:41.915241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:46.600536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:52.918880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:58.530396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:02.915258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:08.519605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:13.448332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:17.826993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:22.784621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:28.165355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:33.025708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:37.855208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:42.257976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:47.096178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:53.449294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:58.902697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:03.315287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:09.400850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:13.772311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:18.210840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:23.231386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:28.563785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:33.472141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:38.228892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:42.602323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:47.547924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:53.859408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:59.313008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:03.715372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:09.773592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:14.207596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:18.553881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:23.618269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:29.024769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:33.890301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:38.582044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:42.957634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:47.941245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:54.255390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:59.656243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:04.236045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:10.130621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:14.575361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:18.933037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:24.028824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:29.432828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:34.362803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:38.924019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:43.302610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:48.373573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:54.623776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:12:00.009411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:04.619112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:10.498892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:14.933289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:19.223848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:24.438244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:29.809936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:34.825217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:39.322063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:43.607292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:48.743988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:54.980854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:12:00.368441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:05.017219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:10.840344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:15.317908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:19.714880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:24.817346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:30.159192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:35.257713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:39.696172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:43.956733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:49.130102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:55.339946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:12:00.702673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:05.446147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:11.241374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:15.670778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:20.034620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:25.197564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:30.521982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:35.668950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:40.066211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:44.692054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:50.379134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T20:11:55.732134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-18T20:12:56.204713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
C1M_FlagC1M_School.closingC2M_FlagC2M_Workplace.closingC3M_Cancel.public.eventsC3M_FlagC4M_FlagC4M_Restrictions.on.gatheringsC5M_Close.public.transportC5M_FlagC6M_FlagC6M_Stay.at.home.requirementsC7M_FlagC7M_Restrictions.on.internal.movementC8EV_International.travel.controlsCityCodeCityNameConfirmedCasesConfirmedDeathsContainmentHealthIndex_AverageCountryCodeCountryNameDateE1_FlagE1_Income.supportE2_Debt.contract.reliefE3_Fiscal.measuresE4_International.supportEconomicSupportIndexGovernmentResponseIndex_AverageH1_FlagH1_Public.information.campaignsH2_Testing.policyH3_Contact.tracingH4_Emergency.investment.in.healthcareH5_Investment.in.vaccinesH6M_Facial.CoveringsH6M_FlagH7_FlagH7_Vaccination.policyH8M_FlagH8M_Protection.of.elderly.peopleJurisdictionMajorityVaccinatedStringencyIndex_AverageV1_Vaccine.Prioritisation..summary.V2A_Vaccine.Availability..summary.V2B_Vaccine.age.eligibility.availability.age.floor..general.population.summary.V2C_Vaccine.age.eligibility.availability.age.floor..at.risk.summary.V2D_Medically..clinically.vulnerable..Non.elderly.V2E_EducationV2F_Frontline.workers...non.healthcare.V2G_Frontline.workers...healthcare.V3_Vaccine.Financial.Support..summary.V4_Mandatory.Vaccination..summary.
C1M_Flag1.0000.3710.4140.0900.0170.3710.4090.1460.0370.4710.3350.1280.2510.1120.2250.2010.2010.1720.1830.0780.4150.4150.0980.0950.0970.0930.0130.0220.1300.0780.0240.0090.2260.0960.0160.0000.2150.3280.0170.1770.2630.1070.3530.0470.1370.1810.1700.1920.1590.0440.0430.0730.0710.1760.078
C1M_School.closing0.3711.0000.1500.5300.6040.1070.1070.4260.4070.1630.0710.4160.1410.3750.3720.3220.3220.0590.0790.5360.2820.2820.3600.1000.4060.2390.0070.0040.3370.5420.0410.3060.2640.2560.0030.0000.3800.0640.0560.3360.1510.3200.0900.4810.6160.3730.3020.3110.3050.2930.3110.2480.2150.2760.206
C2M_Flag0.4140.1501.0000.2980.2090.6230.5670.2120.1450.4400.4740.2080.3380.1560.1310.1890.1890.1800.1980.1630.1920.1920.1030.0780.1020.0980.0220.0250.1700.1380.0800.0000.1510.0300.0180.0000.1190.2780.0500.0950.3260.0890.3650.1080.1740.1510.1750.2380.2330.1050.1030.0920.0570.1410.119
C2M_Workplace.closing0.0900.5300.2981.0000.7050.1540.1470.4970.4500.0880.1290.4590.1830.4390.3570.3240.3240.0480.0710.5770.2470.2470.2990.0790.3630.2270.0120.0110.3090.5720.0300.2430.2580.2590.0120.0000.2950.0590.1430.2750.1510.3050.1130.4420.6770.2900.2420.2660.2600.2600.2720.2370.1680.2080.150
C3M_Cancel.public.events0.0170.6040.2090.7051.0000.2300.1640.6260.4050.0660.1340.5040.1870.4450.4760.3130.3130.0500.0690.6730.2820.2820.4010.0680.3740.2120.0070.0110.3730.6660.0200.2400.2870.2740.0000.0020.3760.0240.0630.3220.1500.3410.0650.4970.7820.2830.2920.3290.3230.2690.2750.2220.1780.2070.187
C3M_Flag0.3710.1070.6230.1540.2301.0000.6580.1540.1230.4370.4930.1560.3670.0980.1310.2170.2170.1870.2090.1360.1730.1730.1640.0110.1370.1020.0230.0250.1660.1210.0750.0090.1420.0320.0210.0000.1220.2600.0510.1390.3280.0960.3670.1470.1540.1750.1930.2450.2440.1260.1180.1010.0580.1390.118
C4M_Flag0.4090.1070.5670.1470.1640.6581.0000.2150.0940.4970.4880.1400.3980.0950.1390.2580.2580.1860.2180.0830.2090.2090.1510.1090.1410.1190.0250.0380.1860.1040.0550.0090.1270.0930.0190.0000.1140.3120.0210.1290.3590.0680.3960.1460.1300.1910.2250.2470.2390.1340.1300.0980.0960.1540.145
C4M_Restrictions.on.gatherings0.1460.4260.2120.4970.6260.1540.2151.0000.3940.1410.1600.3970.1630.4230.2950.2920.2920.0330.0490.4460.2310.2310.2430.1540.3550.2460.0000.0080.2800.4500.0320.1910.2140.2690.0040.0000.2300.1110.0990.2290.1640.2550.1260.4490.5090.3050.2400.2390.2310.2680.2650.2240.1590.1570.168
C5M_Close.public.transport0.0370.4070.1450.4500.4050.1230.0940.3941.0000.2270.1210.4410.0830.3490.3170.3040.3040.0510.0710.5770.2600.2600.2450.1100.2780.2010.0230.0110.3180.5670.0230.1350.2290.1780.0100.0000.2330.0210.1470.2440.0900.2700.1300.3290.5860.2220.2360.2870.2720.1910.2020.1560.1370.1900.089
C5M_Flag0.4710.1630.4400.0880.0660.4370.4970.1410.2271.0000.3830.1490.3350.0870.2280.4990.4990.1510.1740.0960.4740.4740.1770.2170.2100.1220.0050.0130.2620.1310.0480.0120.2600.0560.0110.0000.1650.3130.0440.1760.3510.1560.4090.1760.1650.2300.2890.3500.3470.2090.2200.1520.1510.2080.170
C6M_Flag0.3350.0710.4740.1290.1340.4930.4880.1600.1210.3831.0000.3710.3820.1600.0940.4690.4690.2280.2550.1680.1210.1210.1830.0020.1130.0470.0280.0270.1290.1700.0730.0230.1020.0080.0210.0050.0920.2620.0320.1500.2930.0280.3790.1450.1730.1760.1740.2360.2300.1360.1300.1410.0590.1120.117
C6M_Stay.at.home.requirements0.1280.4160.2080.4590.5040.1560.1400.3970.4410.1490.3711.0000.2020.4150.2900.3700.3700.0650.1040.5470.2520.2520.2370.1860.2810.1850.0200.0070.2660.5360.0520.1580.1530.1850.0090.0010.2850.0390.0480.2420.1730.2560.1330.4180.5460.2560.2130.2910.2900.2720.2660.2290.1730.1630.143
C7M_Flag0.2510.1410.3380.1830.1870.3670.3980.1630.0830.3350.3820.2021.0000.2520.0800.5010.5010.2070.2300.0830.2260.2260.1050.0560.0230.1260.0330.0450.1580.1070.0200.0000.0920.1810.0200.0000.1840.1620.0650.0880.2760.1360.3880.0880.1810.0810.1210.1670.1550.0400.0210.0250.0810.1210.077
C7M_Restrictions.on.internal.movement0.1120.3750.1560.4390.4450.0980.0950.4230.3490.0870.1600.4150.2521.0000.4410.3250.3250.0730.1010.5600.3340.3340.2870.0990.2490.2320.0120.0180.3140.5510.0200.1930.2530.3080.0040.0030.1930.0860.0390.2530.1560.3430.1660.4280.5750.1880.2200.2980.2900.1930.1890.1300.1020.1650.127
C8EV_International.travel.controls0.2250.3720.1310.3570.4760.1310.1390.2950.3170.2280.0940.2900.0800.4411.0000.2560.2560.0760.0620.4760.3340.3340.4060.1300.3580.2880.0000.0110.3230.4860.0400.3960.3230.3750.0090.0000.2700.0760.0390.3380.1310.3090.1770.6550.4830.4320.4000.3140.2930.3080.3170.2210.2000.2700.275
CityCode0.2010.3220.1890.3240.3130.2170.2580.2920.3040.4990.4690.3700.5010.3250.2561.0001.0001.0001.0000.2091.0001.0000.0000.5000.2860.5001.0001.0000.3700.2051.0000.0980.4960.5091.0001.0000.3430.2621.0000.1120.1760.4871.0000.0810.2190.3400.5080.2180.2230.4050.4620.5620.4840.5410.622
CityName0.2010.3220.1890.3240.3130.2170.2580.2920.3040.4990.4690.3700.5010.3250.2561.0001.0001.0001.0000.2091.0001.0000.0000.5000.2860.5001.0001.0000.3700.2051.0000.0980.4960.5091.0001.0000.3430.2621.0000.1120.1760.4871.0000.0810.2190.3400.5080.2180.2230.4050.4620.5620.4840.5410.622
ConfirmedCases0.1720.0590.1800.0480.0500.1870.1860.0330.0510.1510.2280.0650.2070.0730.0761.0001.0001.0000.961-0.1990.1090.1090.5650.0080.0680.0500.0220.020-0.005-0.1860.0060.0850.0540.0910.0180.0240.0630.2120.0000.5340.2270.0620.3890.087-0.2290.0790.0680.0460.0430.0400.0420.0360.0300.1030.137
ConfirmedDeaths0.1830.0790.1980.0710.0690.2090.2180.0490.0710.1740.2550.1040.2300.1010.0621.0001.0000.9611.000-0.1540.1530.1530.4510.0900.0740.0550.0230.0230.042-0.1350.0500.0530.0560.1010.0180.0310.0670.2030.0000.4310.2220.0720.4050.094-0.1570.0780.0730.0650.0630.0580.0600.0630.0310.1270.124
ContainmentHealthIndex_Average0.0780.5360.1630.5770.6730.1360.0830.4460.5770.0960.1680.5470.0830.5600.4760.2090.209-0.199-0.1541.0000.2370.237-0.3240.2210.3860.3340.0540.0120.4800.9800.0790.6980.4540.4960.039-0.0450.4440.1430.103-0.2690.1440.4640.1190.6150.9480.4190.3420.2120.2120.2760.2830.2190.1740.2520.228
CountryCode0.4150.2820.1920.2470.2820.1730.2090.2310.2600.4740.1210.2520.2260.3340.3341.0001.0000.1090.1530.2371.0001.0000.1030.5660.2790.3640.0000.0220.3670.2370.0600.1550.4230.5150.0240.0000.3970.3270.0730.1670.1650.3890.4730.1040.2290.2710.2650.3240.3100.2000.1790.2170.2090.2890.193
CountryName0.4150.2820.1920.2470.2820.1730.2090.2310.2600.4740.1210.2520.2260.3340.3341.0001.0000.1090.1530.2371.0001.0000.1030.5660.2790.3640.0000.0220.3670.2370.0600.1550.4230.5150.0240.0000.3970.3270.0730.1670.1650.3890.4730.1040.2290.2710.2650.3240.3100.2000.1790.2170.2090.2890.193
Date0.0980.3600.1030.2990.4010.1640.1510.2430.2450.1770.1830.2370.1050.2870.4060.0000.0000.5650.451-0.3240.1030.1031.0000.1810.4090.259-0.032-0.017-0.381-0.3760.0430.2160.2320.220-0.045-0.0490.2770.0580.0360.8930.0740.2130.0320.855-0.4460.7200.5900.3580.3580.3800.3630.3010.2340.4800.412
E1_Flag0.0950.1000.0780.0790.0680.0110.1090.1540.1100.2170.0020.1860.0560.0990.1300.5000.5000.0080.0900.2210.5660.5660.1811.0000.2500.2430.0000.0131.0000.2460.0300.0380.1570.1560.0000.0020.1230.0720.0520.1620.0240.1150.0690.0110.1460.1590.2020.3250.3700.0430.1060.0250.0850.1700.059
E1_Income.support0.0970.4060.1020.3630.3740.1370.1410.3550.2780.2100.1130.2810.0230.2490.3580.2860.2860.0680.0740.3860.2790.2790.4090.2501.0000.2640.0050.0130.7970.4380.0200.1960.2820.1690.0040.0020.2640.0710.0350.3570.0360.2060.0990.4120.4310.3730.3560.3240.3200.2050.2270.1860.1300.3260.229
E2_Debt.contract.relief0.0930.2390.0980.2270.2120.1020.1190.2460.2010.1220.0470.1850.1260.2320.2880.5000.5000.0500.0550.3340.3640.3640.2590.2430.2641.0000.0000.0000.8190.4190.0270.1770.2230.3250.0010.0000.1990.1400.0430.2780.0670.2750.1300.3170.3210.2470.2620.2480.2260.1750.1840.1170.0870.2250.162
E3_Fiscal.measures0.0130.0070.0220.0120.0070.0230.0250.0000.0230.0050.0280.0200.0330.0120.0001.0001.0000.0220.0230.0540.0000.000-0.0320.0000.0050.0001.0000.0690.0600.0650.0000.0000.0000.0030.1440.0860.0140.0201.000-0.0290.0160.0000.0280.0000.0760.0000.0000.0000.0000.0000.0000.0100.0120.0000.000
E4_International.support0.0220.0040.0250.0110.0110.0250.0380.0080.0110.0130.0270.0070.0450.0180.0111.0001.0000.0200.0230.0120.0220.022-0.0170.0130.0130.0000.0691.0000.0210.0190.0000.0060.0000.0110.0810.1790.0070.0311.000-0.0190.0340.0000.0390.0000.0230.0020.0000.0000.0000.0000.0000.0000.0000.0000.000
EconomicSupportIndex0.1300.3370.1700.3090.3730.1660.1860.2800.3180.2620.1290.2660.1580.3140.3230.3700.370-0.0050.0420.4800.3670.367-0.3811.0000.7970.8190.0600.0211.0000.6320.0370.2620.2680.3190.0320.0510.2560.1420.071-0.3540.1040.2610.1620.4680.5220.3910.3260.2180.2180.2400.2530.2140.1540.2510.234
GovernmentResponseIndex_Average0.0780.5420.1380.5720.6660.1210.1040.4500.5670.1310.1700.5360.1070.5510.4860.2050.205-0.186-0.1350.9800.2370.237-0.3760.2460.4380.4190.0650.0190.6321.0000.0760.6880.4510.4930.043-0.0300.4360.1160.089-0.3230.1330.4590.1100.6300.9460.4330.3550.2100.2090.2800.2870.2180.1690.2620.245
H1_Flag0.0240.0410.0800.0300.0200.0750.0550.0320.0230.0480.0730.0520.0200.0200.0401.0001.0000.0060.0500.0790.0600.0600.0430.0300.0200.0270.0000.0000.0370.0761.0000.0290.0180.0360.0000.0000.0520.0630.0000.0440.0170.0280.0460.0330.1080.0350.0270.0690.0470.0210.0190.0460.0230.0300.004
H1_Public.information.campaigns0.0090.3060.0000.2430.2400.0090.0090.1910.1350.0120.0230.1580.0000.1930.3960.0980.0980.0850.0530.6980.1550.1550.2160.0380.1960.1770.0000.0060.2620.6880.0291.0000.4080.2430.0000.0000.4630.0310.0000.2090.0800.3340.0440.1700.6780.2020.1890.0690.0690.0460.0480.0340.0320.2100.101
H2_Testing.policy0.2260.2640.1510.2580.2870.1420.1270.2140.2290.2600.1020.1530.0920.2530.3230.4960.4960.0540.0560.4540.4230.4230.2320.1570.2820.2230.0000.0000.2680.4510.0180.4081.0000.4040.0120.0000.3300.0840.0270.2380.0480.2670.2540.3110.3910.3350.2660.2350.2260.1940.2160.2020.1070.2580.255
H3_Contact.tracing0.0960.2560.0300.2590.2740.0320.0930.2690.1780.0560.0080.1850.1810.3080.3750.5090.5090.0910.1010.4960.5150.5150.2200.1560.1690.3250.0030.0110.3190.4930.0360.2430.4041.0000.0000.0000.2710.1470.0170.1590.0070.3600.1640.1690.3820.1180.1450.1920.1790.1210.1000.0690.0690.1150.061
H4_Emergency.investment.in.healthcare0.0160.0030.0180.0120.0000.0210.0190.0040.0100.0110.0210.0090.0200.0040.0091.0001.0000.0180.0180.0390.0240.024-0.0450.0000.0040.0010.1440.0810.0320.0430.0000.0000.0120.0001.0000.1120.0180.0151.000-0.0350.0180.0060.0220.0000.0600.0000.0001.0001.0001.0001.0001.0001.0000.0000.000
H5_Investment.in.vaccines0.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0050.0010.0000.0030.0001.0001.0000.0240.031-0.0450.0000.000-0.0490.0020.0020.0000.0860.1790.051-0.0300.0000.0000.0000.0000.1121.0000.0000.0000.000-0.0410.0000.0020.0030.000-0.0300.0010.0050.0270.0250.0100.0050.0050.0070.0000.000
H6M_Facial.Coverings0.2150.3800.1190.2950.3760.1220.1140.2300.2330.1650.0920.2850.1840.1930.2700.3430.3430.0630.0670.4440.3970.3970.2770.1230.2640.1990.0140.0070.2560.4360.0520.4630.3300.2710.0180.0001.0000.1830.0490.2740.1320.3310.1920.4150.4010.3530.2980.2880.2850.2620.2500.1970.2290.2240.253
H6M_Flag0.3280.0640.2780.0590.0240.2600.3120.1110.0210.3130.2620.0390.1620.0860.0760.2620.2620.2120.2030.1430.3270.3270.0580.0720.0710.1400.0200.0310.1420.1160.0630.0310.0840.1470.0150.0000.1831.0000.0120.0840.2000.1490.3510.0250.1220.0540.0500.1520.1510.0450.0510.0180.0940.0720.024
H7_Flag0.0170.0560.0500.1430.0630.0510.0210.0990.1470.0440.0320.0480.0650.0390.0391.0001.0000.0000.0000.1030.0730.0730.0360.0520.0350.0431.0001.0000.0710.0890.0000.0000.0270.0171.0000.0000.0490.0121.0000.0790.0100.0300.0190.0470.1090.0220.0440.0840.1160.0460.0500.0090.0730.0920.020
H7_Vaccination.policy0.1770.3360.0950.2750.3220.1390.1290.2290.2440.1760.1500.2420.0880.2530.3380.1120.1120.5340.431-0.2690.1670.1670.8930.1620.3570.278-0.029-0.019-0.354-0.3230.0440.2090.2380.159-0.035-0.0410.2740.0840.0791.0000.0730.2010.1010.796-0.4240.7840.6990.5090.4590.6860.6250.5020.4160.5000.409
H8M_Flag0.2630.1510.3260.1510.1500.3280.3590.1640.0900.3510.2930.1730.2760.1560.1310.1760.1760.2270.2220.1440.1650.1650.0740.0240.0360.0670.0160.0340.1040.1330.0170.0800.0480.0070.0180.0000.1320.2000.0100.0731.0000.2770.3920.0150.1740.0930.0960.0820.0640.0210.0180.0290.0380.1050.063
H8M_Protection.of.elderly.people0.1070.3200.0890.3050.3410.0960.0680.2550.2700.1560.0280.2560.1360.3430.3090.4870.4870.0620.0720.4640.3890.3890.2130.1150.2060.2750.0000.0000.2610.4590.0280.3340.2670.3600.0060.0020.3310.1490.0300.2010.2771.0000.1240.2070.4030.2110.1650.2200.2150.1160.1040.0920.0680.1970.091
Jurisdiction0.3530.0900.3650.1130.0650.3670.3960.1260.1300.4090.3790.1330.3880.1660.1771.0001.0000.3890.4050.1190.4730.4730.0320.0690.0990.1300.0280.0390.1620.1100.0460.0440.2540.1640.0220.0030.1920.3510.0190.1010.3920.1241.0000.0300.0980.1190.1560.1610.1780.1620.1200.1400.0900.2750.191
MajorityVaccinated0.0470.4810.1080.4420.4970.1470.1460.4490.3290.1760.1450.4180.0880.4280.6550.0810.0810.0870.0940.6150.1040.1040.8550.0110.4120.3170.0000.0000.4680.6300.0330.1700.3110.1690.0000.0000.4150.0250.0470.7960.0150.2070.0301.0000.6430.7270.7810.7270.7190.6000.6100.4280.3850.6050.459
StringencyIndex_Average0.1370.6160.1740.6770.7820.1540.1300.5090.5860.1650.1730.5460.1810.5750.4830.2190.219-0.229-0.1570.9480.2290.229-0.4460.1460.4310.3210.0760.0230.5220.9460.1080.6780.3910.3820.060-0.0300.4010.1220.109-0.4240.1740.4030.0980.6431.0000.4600.3690.2070.2040.3210.3350.2700.2070.2760.270
V1_Vaccine.Prioritisation..summary.0.1810.3730.1510.2900.2830.1750.1910.3050.2220.2300.1760.2560.0810.1880.4320.3400.3400.0790.0780.4190.2710.2710.7200.1590.3730.2470.0000.0020.3910.4330.0350.2020.3350.1180.0000.0010.3530.0540.0220.7840.0930.2110.1190.7270.4601.0000.8050.4690.4500.4510.4590.3290.2910.6740.414
V2A_Vaccine.Availability..summary.0.1700.3020.1750.2420.2920.1930.2250.2400.2360.2890.1740.2130.1210.2200.4000.5080.5080.0680.0730.3420.2650.2650.5900.2020.3560.2620.0000.0000.3260.3550.0270.1890.2660.1450.0000.0050.2980.0500.0440.6990.0960.1650.1560.7810.3690.8051.0000.9580.8750.6800.7020.5100.5470.5790.492
V2B_Vaccine.age.eligibility.availability.age.floor..general.population.summary.0.1920.3110.2380.2660.3290.2450.2470.2390.2870.3500.2360.2910.1670.2980.3140.2180.2180.0460.0650.2120.3240.3240.3580.3250.3240.2480.0000.0000.2180.2100.0690.0690.2350.1921.0000.0270.2880.1520.0840.5090.0820.2200.1610.7270.2070.4690.9581.0000.7930.6940.7270.5630.5030.1100.327
V2C_Vaccine.age.eligibility.availability.age.floor..at.risk.summary.0.1590.3050.2330.2600.3230.2440.2390.2310.2720.3470.2300.2900.1550.2900.2930.2230.2230.0430.0630.2120.3100.3100.3580.3700.3200.2260.0000.0000.2180.2090.0470.0690.2260.1791.0000.0250.2850.1510.1160.4590.0640.2150.1780.7190.2040.4500.8750.7931.0000.6590.6320.5280.4750.1200.325
V2D_Medically..clinically.vulnerable..Non.elderly.0.0440.2930.1050.2600.2690.1260.1340.2680.1910.2090.1360.2720.0400.1930.3080.4050.4050.0400.0580.2760.2000.2000.3800.0430.2050.1750.0000.0000.2400.2800.0210.0460.1940.1211.0000.0100.2620.0450.0460.6860.0210.1160.1620.6000.3210.4510.6800.6940.6591.0000.7170.5340.4690.0840.297
V2E_Education0.0430.3110.1030.2720.2750.1180.1300.2650.2020.2200.1300.2660.0210.1890.3170.4620.4620.0420.0600.2830.1790.1790.3630.1060.2270.1840.0000.0000.2530.2870.0190.0480.2160.1001.0000.0050.2500.0510.0500.6250.0180.1040.1200.6100.3350.4590.7020.7270.6320.7171.0000.5400.4810.0660.300
V2F_Frontline.workers...non.healthcare.0.0730.2480.0920.2370.2220.1010.0980.2240.1560.1520.1410.2290.0250.1300.2210.5620.5620.0360.0630.2190.2170.2170.3010.0250.1860.1170.0100.0000.2140.2180.0460.0340.2020.0691.0000.0050.1970.0180.0090.5020.0290.0920.1400.4280.2700.3290.5100.5630.5280.5340.5401.0000.2880.1000.234
V2G_Frontline.workers...healthcare.0.0710.2150.0570.1680.1780.0580.0960.1590.1370.1510.0590.1730.0810.1020.2000.4840.4840.0300.0310.1740.2090.2090.2340.0850.1300.0870.0120.0000.1540.1690.0230.0320.1070.0691.0000.0070.2290.0940.0730.4160.0380.0680.0900.3850.2070.2910.5470.5030.4750.4690.4810.2881.0000.3130.202
V3_Vaccine.Financial.Support..summary.0.1760.2760.1410.2080.2070.1390.1540.1570.1900.2080.1120.1630.1210.1650.2700.5410.5410.1030.1270.2520.2890.2890.4800.1700.3260.2250.0000.0000.2510.2620.0300.2100.2580.1150.0000.0000.2240.0720.0920.5000.1050.1970.2750.6050.2760.6740.5790.1100.1200.0840.0660.1000.3131.0000.369
V4_Mandatory.Vaccination..summary.0.0780.2060.1190.1500.1870.1180.1450.1680.0890.1700.1170.1430.0770.1270.2750.6220.6220.1370.1240.2280.1930.1930.4120.0590.2290.1620.0000.0000.2340.2450.0040.1010.2550.0610.0000.0000.2530.0240.0200.4090.0630.0910.1910.4590.2700.4140.4920.3270.3250.2970.3000.2340.2020.3691.000

Missing values

2025-08-18T20:12:01.667572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-18T20:12:05.695570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-18T20:12:12.738048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryNameCountryCodeRegionNameRegionCodeCityNameCityCodeJurisdictionDateC1M_School.closingC1M_FlagC2M_Workplace.closingC2M_FlagC3M_Cancel.public.eventsC3M_FlagC4M_Restrictions.on.gatheringsC4M_FlagC5M_Close.public.transportC5M_FlagC6M_Stay.at.home.requirementsC6M_FlagC7M_Restrictions.on.internal.movementC7M_FlagC8EV_International.travel.controlsE1_Income.supportE1_FlagE2_Debt.contract.reliefE3_Fiscal.measuresE4_International.supportH1_Public.information.campaignsH1_FlagH2_Testing.policyH3_Contact.tracingH4_Emergency.investment.in.healthcareH5_Investment.in.vaccinesH6M_Facial.CoveringsH6M_FlagH7_Vaccination.policyH7_FlagH8M_Protection.of.elderly.peopleH8M_FlagV1_Vaccine.Prioritisation..summary.V2A_Vaccine.Availability..summary.V2B_Vaccine.age.eligibility.availability.age.floor..general.population.summary.V2C_Vaccine.age.eligibility.availability.age.floor..at.risk.summary.V2D_Medically..clinically.vulnerable..Non.elderly.V2E_EducationV2F_Frontline.workers...non.healthcare.V2G_Frontline.workers...healthcare.V3_Vaccine.Financial.Support..summary.V4_Mandatory.Vaccination..summary.ConfirmedCasesConfirmedDeathsMajorityVaccinatedPopulationVaccinatedStringencyIndex_AverageGovernmentResponseIndex_AverageContainmentHealthIndex_AverageEconomicSupportIndex
0AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001010NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
1AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001020NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
2AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001030NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
3AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001040NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
4AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001050NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
5AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001060NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
6AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001070NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
7AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001080NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
8AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001090NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
9AustraliaAUSNaNNaNNaNNaNNAT_TOTAL202001100NaN0NaN0NaN0NaN0NaN0NaN0NaN00NaN00.00.00NaN000.00.00NaN0NaN0NaN00NaNNaNNaNNaNNaNNaN0NaN0.00.0NV00.00.00.00.0
CountryNameCountryCodeRegionNameRegionCodeCityNameCityCodeJurisdictionDateC1M_School.closingC1M_FlagC2M_Workplace.closingC2M_FlagC3M_Cancel.public.eventsC3M_FlagC4M_Restrictions.on.gatheringsC4M_FlagC5M_Close.public.transportC5M_FlagC6M_Stay.at.home.requirementsC6M_FlagC7M_Restrictions.on.internal.movementC7M_FlagC8EV_International.travel.controlsE1_Income.supportE1_FlagE2_Debt.contract.reliefE3_Fiscal.measuresE4_International.supportH1_Public.information.campaignsH1_FlagH2_Testing.policyH3_Contact.tracingH4_Emergency.investment.in.healthcareH5_Investment.in.vaccinesH6M_Facial.CoveringsH6M_FlagH7_Vaccination.policyH7_FlagH8M_Protection.of.elderly.peopleH8M_FlagV1_Vaccine.Prioritisation..summary.V2A_Vaccine.Availability..summary.V2B_Vaccine.age.eligibility.availability.age.floor..general.population.summary.V2C_Vaccine.age.eligibility.availability.age.floor..at.risk.summary.V2D_Medically..clinically.vulnerable..Non.elderly.V2E_EducationV2F_Frontline.workers...non.healthcare.V2G_Frontline.workers...healthcare.V3_Vaccine.Financial.Support..summary.V4_Mandatory.Vaccination..summary.ConfirmedCasesConfirmedDeathsMajorityVaccinatedPopulationVaccinatedStringencyIndex_AverageGovernmentResponseIndex_AverageContainmentHealthIndex_AverageEconomicSupportIndex
239651United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212220NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182581.01958.0V52.923.3730.8535.260.0
239652United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212230NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182581.01958.0V52.923.3730.8535.260.0
239653United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212240NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182581.01958.0V52.923.3730.8535.260.0
239654United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212250NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182581.01958.0V52.923.3730.8535.260.0
239655United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212260NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182581.01958.0V52.923.3730.8535.260.0
239656United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212270NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182847.01958.0V52.923.3730.8535.260.0
239657United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212280NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182847.01958.0V52.9223.3730.8535.260.0
239658United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212290NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182847.01958.0V52.9223.3730.8535.260.0
239659United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212300NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182847.01958.0V52.9223.3730.8535.260.0
239660United StatesUSAWyomingUS_WYNaNNaNSTATE_TOTAL202212310NaN0NaN0NaN0NaN0NaN0NaN11.010NaN0NaNNaN21.031NaNNaN0NaN51.011.0230-4 yrs0-4 yrs2.02.02.02.050.0182847.01958.0V52.9223.3730.8535.260.0